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Introduction We evaluated the incidence of change in serial 12-lead electrocardiogram (ECG) diagnostic classifications in patients resuscitated from out-of-hospital (OH) cardiac arrest (OHCA) comparing OH to emergency department (ED) ECGs. Methods This retrospective case series included: 1) adults (≥ 18 years old), 2) resuscitated from OHCA, 3) ≥ 1 OH and 1 ED ECG/patient, and 4) emergency medical services (EMS) transport to the study hospital. OH and ED ECGs were classified as: 1) STEMI (ST-segment Elevation Myocardial Infarction), 2) Ischemic, and 3) Non-ischemic. Two ED physicians and one cardiologist independently classified all ECGs, then generated a consensus opinion classification for each ECG based on American Heart Association’s 2018 Expert Consensus criteria. The most ischemic OH ECG classification was compared with the last ED ECG classification. Results From 7/27/12 to 7/18/19, 176 patients were entered with a mean age of 61.2 ± 16.6 years; 102/176 (58%) were male. Overall, 504 OH and ED 12-lead ECGs were acquired (2.9 ECGs/patient). ECG classification inter-rater reliability kappa score was 0.63 ± 0.02 (substantial agreement). Overall, 86/176 (49%) changed ECG classification from the OH to ED setting; 69/86 (80%) of these ECGs changed from more to less ischemic classifications. Of 49 OH STEMI ECG classifications, 33/49 (67%) changed to a less ischemic (non-STEMI) ED ECG classification. Conclusions Change in 12-lead ECG classification from OH to ED setting in patients resuscitated from OHCA was common (49%). The OH STEMI classification changed to a less ischemic (non-STEMI) ED classification in 67% of cases.

Delays in motor development are not considered a core feature of autism spectrum disorder (ASD). Yet, recent studies of infant siblings of children with ASD suggest that early delays in motor skills may be associated with later delays in developmental areas considered to be core features of an ASD diagnosis. While these studies demonstrate the longitudinal association between core features and motor delays observed at single time points, there is considerable interest in studying the trajectories of motor development over the first 3 years of life. To accomplish this, we investigated early trajectories of motor development in a cohort of 499 infant siblings of children with ASD and 176 children with no family history of ASD. Data for the current study were drawn from the prospective, multi-site, Canadian Infant Sibling Study. We evaluated trajectories of fine and gross motor development over the first 3 years using group-based trajectory modeling. Our results show that membership for both fine and gross motor trajectory groups was related to expressive language skills, receptive language skills, ASD symptom severity scores, and diagnostic classification at age 3. These results provide evidence that the trajectory of a child's early motor development may have important prognostic implications in ASD.

Postpartum psychosis (PPP) is a serious disorder that is associated with maternal and infant morbidity and mortality, yet it is denied a diagnostic classification in the Diagnostic and Statistical Manual of Mental Disorders (DSM). PPP was included in the DSMI (1952) and the DSMII (1968). It was removed from DSMIII (1980). The purpose of this article is to review the history of postpartum psychosis in the DSM and provide the mechanism and research that argues for the inclusion of postpartum psychosis as a separate diagnostic entity in the DSM. This work describes a proposal to the DSMV committee in 2020 for inclusion of PPP as a unique diagnosis based on the cognitive disorganization that accompanies psychotic symptoms. The proposal for inclusion provides the required criteria such as a rationale for the proposed change and evidence that PPP is distinct from other disorders. Validators are described. Reliability studies and benefits of inclusion are provided. We were unable to gain consensus on the presence of cognitive impairment for all postpartum psychotic episodes. The committee opined that we did not demonstrate proof that PPP is a distinct diagnosis. However, the committee acknowledged that the current "specifier" may be insufficient, and thus the option favored by the committee is that it could be added to Sect. 3 of the DSM, "Conditions for Further Study." In the next proposal, we suggest that the unique temporal relationship of symptom onset to childbirth deserves consideration as unique criteria for diagnostic distinction of PPP.

Interstitial lung diseases (ILDs) are challenging to diagnose, requiring integration of multiple complex features that are often difficult to interpret. This article reviews a pragmatic approach to ILD diagnosis and classification, focusing on diagnostic tools and strategies that are used to separate different subtypes and identify the most appropriate management. We discuss the evolution of ILD classification and the contemporary approach that integrates routinely used diagnostic tools in a multidisciplinary discussion. We highlight the increasing importance of taking a multipronged approach to ILD classification that reflects the recent emphasis on disease behavior while also considering etiopathogenesis and morphologic features.

Attention-deficit/hyperactivity disorder (ADHD) has long been regarded as disparate and mutually exclusive to autism spectrum disorder (ASD) in the Diagnostic and Statistical Manual of Mental Disorders (DSM)-III-R and DSM-IV. However, this idea has become obsolete due to a growing body of evidence suggesting numerous phenotypic and genetic similarities between ADHD and ASD. ASD symptoms or autistic traits in individuals with ADHD have been examined; however, most studies were conducted on children and relied on self- or parent- reports. ASD symptoms assessed with more direct, objective measures, such as the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) in adults with ADHD, remain understudied. In the present study, we used the ADOS-2 to evaluate ASD symptoms in adults with ADHD who were not clinically diagnosed with ASD. Fifty-six adults (mean age 33.9 years, 35 males, intelligence quotient ≥ 85), who were diagnosed with ADHD based on the DSM-5 criteria, completed Module 4 of the ADOS-2. Autism Spectrum Quotient (AQ), Conners' Adult ADHD Rating Scale (CAARS), and Wechsler Adult Intelligence Scale (WAIS)-III were also administered to assess self-rated ASD symptoms, ADHD symptoms, and intelligence, respectively. Overall, 23.3% of participants met the ASD diagnostic classification on the ADOS-2. Social reciprocal interaction scores tended to be higher, while restricted and repetitive behavior scores were low. The scoring patterns and possible overlapping and differing phenotypic characteristics of ADHD and ASD are discussed.

Purpose of review Trigeminal neuralgia is a well-known facial pain syndrome with several treatment options. In contrast, non-neuralgiform idiopathic facial pain syndromes are relatively rare, reflected by the fact that, until 2020, no internationally accepted diagnostic classification existed. Like trigeminal neuralgia, these non-dental facial pain syndromes need to be managed by neurologists and pain specialists, but the lack of pathophysiological understanding has resulted in an underrepresented and undertreated patient group. Recent findings This work provides a brief overview of the most common primary facial pain syndromes, namely, the facial attack-like facial pain, which corresponds to attack-like headache, the persistent idiopathic facial pain (formerly 'atypical facial pain'), and trigeminal neuropathy. What these disorders have in common is that they should all be treated conservatively. Summary On the basis of pragmatic classifications, permanent and attack-like primary facial pain can be relatively easily differentiated from one another. The introduction of the new International Classification of Orofacial Pain offers the opportunity to better coordinate and concentrate scientific efforts, so that in the future the therapy strategies that are still inadequate, can be optimized.

Imaging plays a key role in the management of mediastinal masses. In an effort to standardize the analysis of the mediastinum, the International Thymic Malignancy Interest Group (ITMIG) has proposed a three compartments-based diagnostic classification, intended for clinicians and radiologists. Several articles have documented its usefulness to guide the diagnosis using cross-sectional imaging. Similarly, fluorine-18-radiolabeled fluorodeoxyglucose positron emission tomography combined to computed tomography (18F-FDG-PET/CT) can be useful in this setting, either as a first-line diagnostic technique, or in addition to cross-sectional imaging. In this article, which is thought as an aid for nuclear medicine physicians and radiologists, we aim to present, based on the ITMIG classification, the main mediastinal pathologies that can be observed with 18F-FDG-PET/CT, and the additional diagnostic value that can be expected from this technique. For this purpose, we segmented the mediastinum according to the ITMIG classification, and reviewed the available literature for each of the corresponding organs and/or disease. Given the importance of the clinical context for the interpretation of PET imaging, we presented each of the diseases according to: (1) their suggestive clinical context; (2) the suggestive features on nonenhanced CT (which is the standard in PET imaging); and (3) the typical 18F-FDG characteristics. The purpose of this article is to depict the main features of the most common mediastinal diseases that can be encountered with 18F-FDG-PET/CT, and to highlight its diagnostic value in this setting, alone or in combination with other imaging modalities.

The classification system for neoplasms of the cells lining the renal tubules (renal cell neoplasms) has expanded greatly over the last five decades. The criteria for recognising an entity and including it in the classification have changed from being purely morphological and clinical to include genetics; presently, some are defined purely on genetics. Expansion of the number of entities included in the classification has many of the newly included entities and those under consideration for inclusion being very rare. The clinical utility of including entities which are extremely rare, based mainly upon genetic information, is unclear.

Disorders of consciousness (DOC) are an important but still underexplored entity in neurology. Novel electroencephalography (EEG) measures are currently being employed for improving diagnostic classification, estimating prognosis and supporting medicolegal decision-making in DOC patients. However, complex recording protocols, a confusing variety of EEG measures, and complicated analysis algorithms create roadblocks against broad application. We conducted a systematic review based on English-language studies in PubMed, Medline and Web of Science databases. The review structures the available knowledge based on EEG measures and analysis principles, and aims at promoting its translation into clinical management of DOC patients.

Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common types of cardiomyopathies leading to heart failure. Accurate diagnostic classification of different types of cardiomyopathies is critical for precision medicine in clinical practice. In this study, we hypothesized that machine learning (ML) can be used as a novel diagnostic approach to analyze cardiac transcriptomic data for classifying clinical cardiomyopathies. RNA-Seq data of human left ventricle tissues were collected from 41 DCM patients, 47 ICM patients, and 49 nonfailure controls (NF) and tested using five ML algorithms: support vector machine with radial kernel (svmRadial), neural networks with principal component analysis (pcaNNet), decision tree (DT), elastic net (ENet), and random forest (RF). Initial ML classifications achieved ~93% accuracy (svmRadial) for NF vs. DCM, ~82% accuracy (RF) for NF vs. ICM, and ~80% accuracy (ENet and svmRadial) for DCM vs. ICM. Next, 50 highly contributing genes (HCGs) for classifying NF and DCM, 68 HCGs for classifying NF and ICM, and 59 HCGs for classifying DCM and ICM were selected for retraining ML models. Impressively, the retrained models achieved ~90% accuracy (RF) for NF vs. DCM, ~90% accuracy (pcaNNet) for NF vs. ICM, and ~85% accuracy (pcaNNet and RF) for DCM vs. ICM. Pathway analyses further confirmed the involvement of those selected HCGs in cardiac dysfunctions such as cardiomyopathies, cardiac hypertrophies, and fibrosis. Overall, our study demonstrates the promising potential of using artificial intelligence via ML modeling as a novel approach to achieve a greater level of precision in diagnosing different types of cardiomyopathies.

Il contributo si pone l'obiettivo di evidenziare la dannosità e pervasività del Complex Trauma sullo sviluppo del bambino, attraverso l'esposizione di un caso clinico esemplifica-tivo, sia per l'analisi della compromissione funzionale della vittima sia per porre l'attenzione sui limiti dell'attuale sistema di classificazione diagnostico, che ad oggi fa riferimento uni-camente alla diagnosi di PTSD (Post Traumatic Stress Disorder), ritenuta non sufficiente-mente esaustiva per descrivere e riconoscere i traumi relazionali precoci, multipli e cronici, responsabili dei diversi possibili esiti disadattivi. Diversamente, il Disturbo Traumatico del-lo Sviluppo (DTS), non ancora inserito nei manuali diagnostici, viene ritenuto più consono a descrivere la complessità degli effetti del trauma interpersonale precoce e cronico, consi-derando ed includendo le molteplici espressioni sintomatiche del disturbo e, di conseguen-za, individuando i percorsi terapeutici più adeguati per i bambini.

Objective Value in evaluating error subtypes on visuospatial line orientation tests has been reported. Directional bias metrics for line orientation test errors represent easily quantifiable data that have not previously been studied. We evaluated whether patients with a clinical condition known to affect visuospatial functioning (Parkinson’s disease [PD]) exhibited unique directional error patterns on the RBANS Line Orientation test relative to other neuropsychology-referred patients. Method We compared overall directional bias in errors, directional bias by line location (left or right line and visual field), and absolute error rates (regardless of direction) by line location in a retrospective sample of patients with PD and a sample of neuropsychology-referred patients without PD. Groups were roughly matched on age, education, gender, and overall level of cognitive impairment. Results Patients with PD exhibited higher rates of leftward bias in errors, both overall and for the left stimulus line in each pair. Directional bias error scores better predicted PD versus non-PD group status than RBANS Line Orientation raw scores. Classification accuracy data for these variables were modest in the entire sample but stronger in a subsample of patients with mild levels of overall cognitive impairment. Conclusions Directional bias metrics for line orientation tests represent easily quantifiable data with potential theoretical and clinical value. In our sample, patients with PD made more left-biased line orientation errors than other neuropsychology-referred patients. By themselves, directional bias scores may have limited diagnostic potential, but they may be useful in diagnostic classification models and may have implications for clinical care.

Glioblastoma (GBM) is the most deadly primary brain tumour and is a paradigmatic example of heterogeneous cancer. Although expanding data propose the phenotypic plasticity exhibited by glioblastoma cells, as a critical feature involved in the tumour development and posttherapy recurrence, the central machinery responsible for their aggressiveness remains elusive. Despite decades of research, the complex biology of the glioblastoma is still unknown. Progress in genetic and epigenetic discoveries has improved diagnostic classification, prognostic information, and therapeutic planning. In the complex model of intercellular signalling, several studies have shown that extracellular vesicles have a key role in the intercellular communication among GBM cells and the tumour microenvironment modulation. The purpose of this review is to summarize the role of the EV-mediated intercellular crosstalk in the glioblastoma physiopathology.

The definitions of most of the currently recognized neurodevelopmental disorders and the criteria used to identify them have seen important changes since their inclusion in diagnostic classification systems (see, for instance, how the definitions of specific learning disorders and autism spectrum disorder (ASD) have been revised in successive versions of DSM). As is the case with many other mental conditions, our understanding of neurodevelopmental disorders is continuously being updated in the light of new research findings. However, this has not been the case for nonverbal learning disability. More than 50 years since it was first described,1 there is still no consensus on the merits of identifying it as a separate disorder or using a clear and acknowledged diagnostic label. Instead of trying to establish why nonverbal learning disability has yet to be included in the diagnostic manuals, this editorial examines why recognized criteria for the condition would improve research in this field and avoid the negative consequences of continuing to conduct research without adopting shared criteria.

Background: With the advances in neuroimaging in amyotrophic lateral sclerosis (ALS), it has been speculated that multiparametric magnetic resonance imaging (MRI) is capable to contribute to early diagnosis. Machine learning (ML) can be regarded as the missing piece that allows for the useful integration of multiparametric MRI data into a diagnostic classifier. The major challenges in developing ML classifiers for ALS are limited data quantity and a suboptimal sample to feature ratio which can be addressed by sound feature selection. Methods: We conducted a systematic review to collect MRI biomarkers that could be used as features by searching the online database PubMed for entries in the recent 4 years that contained cross-sectional neuroimaging data of subjects with ALS and an adequate control group. In addition to the qualitative synthesis, a semi-quantitative analysis was conducted for each MRI modality that indicated which brain regions were most commonly reported. Results: Our search resulted in 151 studies with a total of 221 datasets. In summary, our findings highly resembled generally accepted neuropathological patterns of ALS, with degeneration of the motor cortex and the corticospinal tract, but also in frontal, temporal, and subcortical structures, consistent with the neuropathological four-stage model of the propagation of pTDP-43 in ALS. Conclusions: These insights are discussed with respect to their potential for MRI feature selection for future ML-based neuroimaging classifiers in ALS. The integration of multiparametric MRI including DTI, volumetric, and texture data using ML may be the best approach to generate a diagnostic neuroimaging tool for ALS.

BackgroundDiagnostic and patients’ management modifications induced by whole-body 18F-FDG-PET/CT had not been evaluated so far in prosthetic valve (PV) or native valve (NV) infective endocarditis (IE)-suspected patients.MethodsIn sum, 140 consecutive patients in 8 tertiary care hospitals underwent 18F-FDG-PET/CT. ESC-2015-modified Duke criteria and patients’ management plan were established jointly by 2 experts before 18F-FDG-PET/CT. The same experts reestablished Duke classification and patients’ management plan immediately after qualitative interpretation of 18F-FDG-PET/CT. A 6-month final Duke classification was established.ResultsAmong the 70 PV and 70 NV patients, 34 and 46 were classified as definite IE before 18F-FDG-PET/CT. Abnormal perivalvular 18F-FDG uptake was recorded in 67.2% PV and 24.3% NV patients respectively (P < .001) and extracardiac uptake in 44.3% PV and 51.4% NV patients. IE classification was modified in 24.3% and 5.7% patients (P = .005) (net reclassification index 20% and 4.3%). Patients’ managements were modified in 21.4% PV and 31.4% NV patients (P = .25). It was mainly due to perivalvular uptake in PV patients and to extra-cardiac uptake in NV patients and consisted in surgery plan modifications in 7 patients, antibiotic plan modifications in 22 patients and both in 5 patients. Altogether, 18F-FDG-PET/CT modified classification and/or care in 40% of the patients (95% confidence interval: 32–48), which was most likely to occur in those with a noncontributing echocardiography (P < .001) or IE classified as possible at baseline (P = .04), while there was no difference between NV and PV.ConclusionsSystematic 18F-FDG-PET/CT did significantly and appropriately impact diagnostic classification and/or IE management in PV and NV-IE suspected patients.Clinical Trials RegistrationNCT02287792.

AIMS To determine whether HbA1c mismatches (HbA1c levels that are higher or lower than expected for the average glucose levels in different individuals) could lead to errors if diagnostic classification is based only on HbA1c levels. METHODS In a cross-sectional study, 3106 participants without known diabetes underwent a 75-g oral glucose tolerance test (fasting glucose and 2-h glucose) and a 50-g glucose challenge test (1-h glucose) on separate days. They were classified by oral glucose tolerance test results as having: normal glucose metabolism; prediabetes; or diabetes. Predicted HbA1c was determined from the linear regression modelling the relationship between observed HbA1c and average glucose (mean of fasting glucose and 2-h glucose from the oral glucose tolerance test, and 1-h glucose from the glucose challenge test) within oral glucose tolerance test groups. The haemoglobin glycation index was calculated as [observed - predicted HbA1c ], and divided into low, intermediate and high haemoglobin glycation index mismatch tertiles. RESULTS Those participants with higher mismatches were more likely to be black, to be men, to be older, and to have higher BMI (all P

Vertebral fracture (VF) is the most common type of osteoporotic fracture. VFs are associated with a decline in quality of life and high morbidity and mortality. The presence of a VF is a significant risk factor for developing another fracture; however, most VFs are not clinically recognized and diagnosed. Vertebral fracture assessment by dual-energy X-ray absorptiometry is a low cost, low radiation, convenient, and reliable method to identify VFs. The finding of a previously unrecognized VF may change the assessment of fracture risk, diagnostic classification, and treatment strategies. Vertebral fracture assessment or radiographic lateral spine imaging should be repeated in patients with continued high risk for fracture (e.g., historical height loss >4 cm [>1.5 inches], self-reported but undocumented vertebral fracture, or glucocorticoid therapy equivalent to ≥5 mg of prednisone or equivalent per day for greater than or equal to 3 months).

Os transtornos alimentares (TA) são caracterizados por um distúrbio persistente do comportamento alimentar ou relacionado à alimentação, que resulta em consumo ou absorção alterados de alimentos e prejudica significativamente a saúde física ou o funcionamento psicossocial. Para uma adequada classificação e categorização dos TA, manuais de critérios diagnósticos descrevem os TA e seus critérios diagnósticos, de modo a orientar o clínico na sua prática para um diagnóstico preciso. Assim, os principais sistemas classificatórios atuais – a 5ª edição do Manual Diagnóstico e Estatístico de Transtornos Mentais (DSM-5) e a 11ª edição da Classificação Internacional de Doenças (CID-11) – trazem um capítulo destinado aos TA. No presente artigo, trazemos as definições dos TA, bem como as características, semelhanças e diferenças entre os dois manuais diagnósticos, de modo a guiar o clínico em sua prática cotidiana.

Soft tissue sarcomas encompass a broad spectrum of histologically, clinically, and molecularly diverse neoplasms that present unique diagnostic and therapeutic challenges. Accurate classification is essential both for appropriate risk stratification and for guiding clinical management. Once classified almost exclusively based on the morphologic appearance of the tumor by light microscopy, many soft tissue sarcomas are now known to manifest recurrent patterns of genetic alterations. In addition to enabling molecular confirmation of histologic diagnoses, discovery of these recurrent genetic alterations has helped to refine existing morphologic definitions of sarcoma subtypes and even prompted the discovery of new subtypes. As therapy for sarcoma has become increasingly tailored to a specific entity, the integration of molecular data has assumed added importance in diagnostic decision making. In this article, we summarize principles of the histologic evaluation of soft tissue sarcomas, discuss specific diagnostic features of several of the most common sarcoma subtypes, and describe our vision for a future of soft tissue sarcoma diagnosis that merges morphologic, genetic, and epigenetic features to arrive at diagnoses that are aligned with tumor-specific, biologically targeted treatment approaches.

Mature B- and T-cell lymphomas are diverse in their biology, etiology, genetics, clinical behavior, and response to specific therapies. Here, we review the principles of diagnostic classification for non-Hodgkin lymphomas, summarize the characteristic features of major entities, and place recent biological and molecular findings in the context of principles that are applicable across the spectrum of mature lymphoid cancers.

Purpose Fabry disease belongs to lysosomal storage disorders and can be successfully treated today. On the contrary, the correct diagnostic classification of its symptoms can be challenging and most patients suffer from pain for years, until they are diagnosed correctly. The aim of this project was to characterize patients with unclassified extremity pain and to present a simple algorithm for a retrospective stratification approach. Patients and methods The FabryScan includes a bedside-test and a questionnaire, consisting of 10 symptom-orientated and anamnestic questions. For the stratification of patients according to the likelihood for Fabry disease two different approaches were conducted. First, a prospective subgrouping based on the previously invented FabryScan evaluation system was conducted. The second retrospective approach consisted of a factor analysis and a subsequent two-way cluster analysis. Further on, 4 patients diagnosed with Fabry disease were stratified according to both approaches. Results In total, 183 completed datasets were included in the statistical analysis. The first approach prospectively classified patients into 3 subgroups (n=40 [likely], n=96 [possible], n=47 [unlikely]) according to the FabryScan evaluation system. The second approach retrospectively stratified patients into 3 subgroups (n=47 [cluster 1], n=95 [cluster 2], n=41 [cluster 3]). Finally, the Fabry patients were sorted to the subgroups, indicative for the highest possibility of Fabry disease in both stratification approaches A and B. Conclusion Both stratification approaches sorted patients with confirmed Fabry disease to the subgroups, indicative for the highest likelihood for Fabry. These results indicate validity of the initially selected FabryScan outcome parameters.

Objective To analyse the application of a new narrow-band imaging (NBI) classification in the diagnosis of vocal cord leukoplakia by laryngologists with different levels of laryngoscopic experience and to explore the impact of NBI training programmes on laryngologists' identification of benign and malignant leukoplakia. Design Prospective multicentre study. Setting Tertiary hospitals. Participants Sixteen laryngologists were divided into less-experienced and experienced groups and received NBI training course. Thirty cases of vocal cord leukoplakia were investigated. Main outcome measures Diagnostic accuracy and interobserver agreement under white light imaging (WLI), before and after NBI training, were analysed among doctors with varying levels of experience. Results The accuracy in the less-experienced group was significantly lower than that of experience group (0.59 vs 0.69) under WLI. There was no significant difference in the diagnostic accuracy between the less-experienced group and the experienced group before NBI training (0.75 vs 0.74) and after NBI training (0.79 vs 0.83). NBI training could improve the interobserver agreement from fair or moderate to good agreement. Conclusion The new NBI diagnostic classification is helpful for identifying benign and malignant vocal cord leukoplakia. In addition, the NBI training programme can improve the diagnostic accuracy and interobserver agreement of less-experienced doctors to the level of experienced laryngologists.

ObjectiveTo identify migraineurs and headache-free individuals with an online questionnaire and automated analysis algorithm.MethodsWe created a branching-logic, web-based questionnaire—the Penn Online Evaluation of Migraine (POEM)—to obtain standardized headache history from a previously studied cohort. Responses were analyzed with an automated algorithm to assign subjects to one of several categories based on ICHD-3 (beta) criteria. Following a pre-registered protocol, this result was compared to prior diagnostic classification by a neurologist following a direct interview.ResultsOf 118 subjects contacted, 90 (76%) completed the questionnaire; of these 31 were headache-free, 29 migraine without aura (MwoA), and 30 migraine with aura (MwA). Mean age was 41 ± 6 years and 76% were female. There were no significant demographic differences between groups. The median time to complete the questionnaire was 2.5 minutes. Sensitivity of the POEM tool was 42%, 59%, and 70%, and specificity was 100%, 84%, and 94% for headache-free, MwoA, and MwA, respectively. Sensitivity and specificity of the POEM tool for migraine overall (with or without aura), was 83% and 90%, respectively.ConclusionsThe POEM web-based questionnaire, and associated analysis routines, identifies headache-free and migraine subjects with good specificity. It may be useful for classifying subjects for large-scale research studies.Trial Registration:https://osf.io/sq9ef

Over the last decade, advances in molecular and imaging-based biomarkers have induced a more versatile diagnostic classification and prognostic evaluation of glioma patients. This, in combination with a growing therapeutic armamentarium, enables increasingly individualized, risk-benefit-optimized treatment strategies. This path to precision medicine in glioma patients requires surgical procedures to be reassessed within multidimensional management considerations. This article attempts to integrate the surgical intervention into a dynamic network of versatile diagnostic characterization, prognostic assessment, and multimodal treatment options in the light of the latest 2016 World Health Organization (WHO) classification of diffuse brain tumors, WHO grade II, III, and IV. Special focus is set on surgical aspects such as resectability, extent of resection, and targeted surgical strategies including minimal invasive stereotactic biopsy procedures, convection enhanced delivery, and photodynamic therapy. Moreover, the influence of recent advances in radiomics/radiogenimics on the process of surgical decision-making will be touched.

During the last decade, the genomics revolution has driven critical advances in molecular oncology and pathology, and a deeper appreciation of heterogeneity that is beginning to reshape our thinking around diagnostic classification. Recent developments have seen existing classification systems modified and improved where possible, gene-based diagnostics implemented and tumour-immune interactions modulated. We present a detailed discussion of this progress, including advances in the understanding of breast tumour classification, e.g. mixed ductal-lobular tumours and the spectrum of triple-negative breast cancer. The latest information on clinical trials and the implementation of gene-based diagnostics, including MammaPrint and Oncotype Dx and others, is synthesised, and emerging targeted therapies, as well as the burgeoning immuno-oncology field, and their relevance in breast cancer, are discussed. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Background Drift is a well-known issue affecting intracoronary pressure measurements. A small pressure offset at the end of the procedure is generally considered acceptable, while repeat assessment is advised for drift exceeding ±2 mmHg. This practice implies that drift assessed after wire pullback equals that at the time of stenosis appraisal, but this assumption has not been systematically investigated. Our aim was to compare intra-and post-procedural pressure sensor drift and assess benefits of correction for intra-procedural drift and its effect on diagnostic classification. Methods In 70 patients we compared intra- and post-procedural pressure drift for 120 hemodynamic tracings obtained at baseline and throughout the hyperemic response to intracoronary adenosine. Intra-procedural drift was derived from the intercept of the stenosis pressure gradient-velocity relationship. Diagnostic reclassification after correction for intra-procedural drift was assessed for the mean distal-to-aortic pressure ratio at baseline (Pd/Pa) and hyperemia (fractional flow reserve, FFR), and corresponding stenosis resistances. Results Post- and intra-procedural drift exceeding the tolerated threshold was observed in 73% and 64% of the hemodynamic tracings, respectively. Discordance in terms of acceptable drift level was present for 42% of the tracings, with avoidable repeat physiological assessment in 25% and unacceptable intra-procedural drift unrecognized at final drift check in 17% of the tracings. Correction for intra-procedural drift caused higher reclassification rates for baseline than hyperemic functional indices. Conclusions Post-procedural pressure drift frequently does not match drift during physiological assessment. Tracing-specific correction for intra-procedural drift can potentially lower the risk of inadvertent diagnostic misclassification and prevent unnecessary repeats.

Recently, it has become a main consensus that early diagnosis of benign prostatic hyperplasia (BPH) is meaningful in delaying disease progression. Although ultrasound has been well recognized as the most useful early screening technique, the lack of a standard observation plane makes it difficult to determine the BPH stages in a way that is operator-independent and satisfies clinical expectations. Strong enthusiasm has fueled intense research on ultrasound coupled with artificial intelligence (AI) for automated or semiautomated classification of different clinical diseases. Therefore, it is crucial to formulate standard planes for ultrasound imaging with the implementation of AI to evaluate BPH. This study was a preliminary examination of AI-assisted ultrasound in the prostate disease diagnostic classification task, which aimed to define and assess the clinical value of the length of the proximal prostatic urethra (Lppu), detected via transrectal ultrasound within the uniquely calibrated standardized plane, in 643 patients suffering from BPH between January 2015 and December 2016. International Prostate Symptom Score (IPSS), quality of life (QoL) and PSA levels were also recorded. The mean (standard deviation) of Lppu was 28.54 mm (9.19 mm), and there were significant differences in the mean Lppu among men who were over 50 yrs old. Lppu was colinear with IPSS, voiding-IPSS, storage-IPSS and QoL with individual r values of 0.13, 0.09, 0.04, and 0.03, respectively (P

Purpose: This study was conducted in order to compare the diagnostic classification of Bruch's membrane opening-minimum rim width (BMO-MRW) and RNFL thickness in normal myopic subjects by using optical coherence tomography (OCT).Methods: This cross-sectional study involved 75 healthy myopic subjects [spherical equivalent (SE) ≤ −0.5D] from April 2019 to January 2020. One eye of each subject was randomly selected for examination. BMO-MRW and peripapillary RNFL thickness were measured by spectral-domain OCT (Spectralis, Heidelberg Engineering GmbH, Heidelberg, Germany). All the subjects were divided into three groups: low myopic group (SE > −3D), moderate myopic group (−6D < SE ≤ −3D), and high myopic group (SE ≤ −6D). A nonparametric test was used to analyze the difference among groups. Linear regression was used to analyze the relationship between BMO-MRW/RNFL thickness and axial length/spherical equivalent. McNemar test was used to compare the diagnostic classification between BMO-MRW and RNFL thickness.Results: The RNFL thickness classified a significantly higher percentage of eyes as outside normal limits/borderline in at least 1 quadrant (BMO-MRW, 4%; RNFL thickness, 34.67%; p < 0.01). There was no significant correlation between BMO-MRW/RNFL thickness and AL/SE. The low myopia (SE > −3D) had a significantly lower percentage of eyes classified as outside normal limits/borderline in at least 1 quadrant than the moderate myopia (−6D < SE ≤ −3D) and high myopia (SE ≤ −6D) (low myopia, 12.5%; moderate/high myopia, 42.42%/50%; p < 0.05).Conclusion: BMO-MRW had a lower percentage of eyes classified as outside normal limits/borderline in at least 1 quadrant than RNFL thickness in normal myopic subjects. When referring to the diagnostic classification of RNFL thickness in myopic subjects, caution should be exercised in interpreting positive results. Further studies are needed to compare the diagnostic accuracy of these two measurements in myopic glaucoma patients.

General breast cancer detection contains two steps, the breast abnormality classification, and the diagnostic classification. The determination of the abnormality contributes further to the following steps, and computational technologies can aid in the process. A lot of machine learning methods have been applied to automate the detection. However, most of them focus on the diagnostic classification and the breast abnormality classification only attracts little attention. The insufficient size of public mammogram datasets also limits the performance of many machine learning algorithms. Considering the importance of breast abnormality classification and the shortage of public large-scale medical datasets, we proposed a meta-learning-based breast abnormality classification method. Our model referred to the latest work of meta-learning-based image classifier and modified it. Specifically, we applied the idea of meta-learning to retrain a pretrained embedding neural network in order to adapt its feature extraction ability to the CBIS-DDSM dataset [1]. The dataset contains two types of abnormal breast mammograms, mass and calcification, and each type is made of two categories of medical images, full mammograms, and ROI [2]. The application of the data augmentation techniques and the idea of meta-learning helped to deal with the insufficient training sample problem and showed a final accuracy of 76%, which beat the 71% accuracy reached by a neural network baseline model.

Insomnia is the most common sleep disorder in the adult population. However, the definition of insomnia disorder has varied across major classification systems and changed over time. In the present study, the investigators traced the evolution of insomnia disorder across classification systems, contemplated the empirical basis for its current definitions, and surveyed ongoing research efforts that may clarify insomnia nosology in the future. Three major classification systems for insomnia are the International Classification of Sleep Disorders, the International Classification of Diseases, and DSM. Despite their divergent origins, these classification systems have converged to nearly identical contemporary insomnia definitions. Over time, the emphasis in classification approaches has shifted from symptomatology to etiology to treatment implications. Additionally, the historical multitude of insomnia subtypes has gradually consolidated into a few core diagnoses, reflecting inadequate evidence with which to support subtyping. Current insomnia definitions include frequency and duration criteria to operationalize these diagnoses, while the diagnostic criterion of nonrestorative sleep has been eliminated (with some controversy). In ongoing research efforts, the quest for insomnia biomarkers has not thus far yielded clinically deployable breakthroughs. Data-driven insomnia subtyping suggests a promising new approach in deriving empirically based subtypes; conversely, the transdiagnostic perspective proposes the elimination of categorical distinctions in favor of finding common processes underlying all psychiatric disorders. The continual evolution of insomnia nosology highlights that much remains to be learned about these conditions; all current diagnostic classification systems are best regarded as "works in progress." Nevertheless, refinement and convergence of classification approaches is essential to standardizing insomnia research, diagnosis, and treatment.

Aberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper, we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites. We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis. The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types. These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.

Sensory processing and motor coordination atypicalities are not commonly identified as primary characteristics of autism spectrum disorder (ASD), nor are they well captured in the NIMH’s original Research Domain Criteria (RDoC) framework. Here, motor and sensory features performed similarly to RDoC features in support vector classification of 30 ASD youth against 33 typically developing controls. Combining sensory with RDoC features boosted classification performance, achieving a Matthews Correlation Coefficient (MCC) of 0.949 and balanced accuracy (BAcc) of 0.971 (p = 0.00020, calculated against a permuted null distribution). Sensory features alone successfully classified ASD (MCC = 0.565, BAcc = 0.773, p = 0.0222) against a clinically relevant control group of 26 youth with Developmental Coordination Disorder (DCD) and were in fact required to decode against DCD above chance. These findings highlight the importance of sensory and motor features to the ASD phenotype and their relevance to the RDoC framework.

Background New hemocytometric parameters can be used to differentiate causes of acute febrile illness (AFI). We evaluated a software algorithm–Infection Manager System (IMS)—which uses hemocytometric data generated by Sysmex hematology analyzers, for its accuracy to detect bacteremia in AFI patients with and without malaria in Burkina Faso. Secondary aims included comparing the accuracy of IMS with C-reactive protein (CRP) and procalcitonin (PCT). Methods In a prospective observational study, patients of ≥ three-month-old (range 3 months– 90 years) presenting with AFI were enrolled. IMS, blood culture and malaria diagnostics were done upon inclusion and additional diagnostics on clinical indication. CRP, PCT, viral multiplex PCR on nasopharyngeal swabs and bacterial- and malaria PCR were batch-tested retrospectively. Diagnostic classification was done retrospectively using all available data except IMS, CRP and PCT results. Findings A diagnosis was affirmed in 549/914 (60.1%) patients and included malaria (n = 191) bacteremia (n = 69), viral infections (n = 145), and malaria-bacteremia co-infections (n = 47). The overall sensitivity, specificity, and negative predictive value (NPV) of IMS for detection of bacteremia in patients of ≥ 5 years were 97.0% (95% CI: 89.8–99.6), 68.2% (95% CI: 55.6–79.1) and 95.7% (95% CI: 85.5–99.5) respectively, compared to 93.9% (95% CI: 85.2–98.3), 39.4% (95% CI: 27.6–52.2), and 86.7% (95% CI: 69.3–96.2) for CRP at ≥20mg/L. The sensitivity, specificity and NPV of PCT at 0.5 ng/ml were lower at respectively 72.7% (95% CI: 60.4–83.0), 50.0% (95% CI: 37.4–62.6) and 64.7% (95% CI: 50.1–77.6) The diagnostic accuracy of IMS was lower among malaria cases and patients, Author summary This study describes the diagnostic accuracy of the Infection Manager System (IMS), a novel diagnostic algorithm for febrile illnesses that is equipped on a routine hematology analyzer. The latest generation hematology analyzers allow better differentiation between leukocyte subsets and their phenotype. The IMS was created, using differences in immune cell subsets (their activation status for instance), to differentiate viral from bacterial etiologies of fever. Such a tool may guide clinicians in their decision the initiate or withhold antimicrobial therapy. The study was carried out among febrile patients aged 3 months and older in rural Burkina Faso, a sub-Saharan African setting where malaria is endemic. Standard microbiological techniques such as blood culture were used as a reference to assess the diagnostic accuracy of IMS. We then compared the diagnostic accuracy of the IMS with the marketed biomarkers C-reactive protein (CRP) and procalcitonin (PCT). Our study showed that the diagnostic performance of the IMS was similar to CRP and better than PCT to detect bacteremia in patients with and without malaria co-infection. Further studies are needed to see if the IMS can be safely used to guide initial antimicrobial treatment and help to reduce further spread of antimicrobial resistance.

Osteoporosis has been defined as a skeletal disease characterized by compromised bone strength predisposing to an increased risk of fracture. Osteoporosis can be diagnosed in the setting of a low trauma fracture. It is preferable to make a diagnosis of osteoporosis in a patient prior to the occurrence of fracture. Dual-energy X-ray absorptiometry (DXA) is the standard of care for diagnosis of osteoporosis. There is general consensus for screening women 65 years and older. The National Osteoporosis Foundation guidelines recommend screening for: (1) women ≥65 years of age; (2) men ≥70 years of age; and (3) men and postmenopausal women ≥50 years of age with at least one risk factor for fracture. Risk factors include previous fracture, long-term glucocorticoid therapy, low body weight, family history of hip fracture, cigarette smoking, and excess alcohol intake. While vertebral fractures are the most common fragility fracture, the majority do not present with clinical symptoms. Identification of a previously undetected vertebral fracture can change the diagnostic classification of a patient and may guide the choice of initial therapy. Various guidelines recommend screening for vertebral fractures in older women and men, in patients with height loss, and in patients taking glucocorticoids.

Five years after the publication of DSM-5 in 2013, three widely used diagnostic instruments have published algorithms designed to represent its (sub-)criteria for Autism Spectrum Disorder (ASD) in children and adolescents. This study aimed to: (1) establish the content validity of these three DSM-5-adapted algorithms, and (2) identify problems with the operationalization of DSM-5 diagnostic criteria in measurable and observable behaviors. Algorithm items of the Autism Diagnostic Observation Schedule-Second Edition (ADOS-2), Developmental, Dimensional and Diagnostic Interview (3di) and Diagnostic Interview for Social and Communication Disorders-11th edition (DISCO-11) were mapped onto DSM-5 sub-criteria. The development and decision-making rules integrated in their algorithms were then compared with DSM-5. Results demonstrated significant variability in the number and nature of sub-criteria covered by the ADOS-2, 3di and DISCO-11. In addition to differences in the development of algorithms and cut-off scores, instruments also differed in the extent to which they follow DSM-5 decision-making rules for diagnostic classification. We conclude that such differences in interpretation of DSM-5 criteria provide a challenge for symptom operationalization which will be most effectively overcome by consensus, testing and reformulation. ispartof: EUROPEAN CHILD & ADOLESCENT PSYCHIATRY vol:30 issue:1 pages:75-87 ispartof: location:Germany status: published

This research reviews 18 scientific articles concerning the application of infrared thermography (IRT) in the mensuration of diagnostic studies of carpal tunnel syndrome (CTS). In addition, the proposed future challenges in this research area are identified. A review of articles is performed in databases such as PubMed, Scopus, EBSCO, ELSEVIER, Springer, and Oxford Academic using the keywords: carpal tunnel syndrome and (thermography OR infrared image OR thermal image). Its contents, journals publishing the topic, and the year of publication are reviewed, and graphs and cross tables are constructed. Using databases such as PubMed, Scopus, EBSCO, ELSEVIER, Springer, and Oxford Academic, 937 articles are identified, 37 of which were duplicates. The titles and abstracts of the remaining articles were reviewed, and 855 articles were deleted due to exclusion criteria. Eighteen articles were found written in foreign language, five were removed for not covering the topic (three reviews and two on liquid crystal thermography), and four were not available online. Finally, eighteen articles were selected for the full text review, from which 13 articles meet the CTS diagnostic classification and 5 consider the CTS studies. IRT is a reliable method in the diagnosis of CTS, mainly in the first stage. To improve diagnostic accuracy, it is recommended nerve conduction studies.

Patients with osteoporosis should be evaluated for factors contributing to skeletal fragility, falls, and fracture risk. Some patients with a T-score value consistent with a diagnostic classification of osteoporosis may in fact have another skeletal disease (e.g., osteomalacia) that requires treatment that is different than osteoporosis. Others may have medical conditions, such as calcium or vitamin D deficiency, that must be corrected before pharmacological therapy to reduce fracture risk is started. Evaluation of all patients with osteoporosis includes a focused medical history, physical examination, and basic laboratory testing. Many patients with osteoporosis should have a vertebral imaging to identify vertebral fractures since they frequently do not cause acute symptoms but still suggest that the osteoporosis is more severe than suggested by the T-score alone. For patients with unusual clinical features or abnormalities on initial assessment, more extensive laboratory testing, imaging studies, or, in rare cases, tetracycline-labeled transiliac bone biopsy may be indicated. The findings of the osteoporosis evaluation allow treatment decisions to be individualized in order to optimize the potential for favorable clinical outcomes.

Expert radiologists can quickly extract a basic "gist" understanding of a medical image following less than a second exposure, leading to above-chance diagnostic classification of images. Most of this work has focused on radiology tasks (such as screening mammography), and it is currently unclear whether this pattern of results and the nature of visual expertise underlying this ability are applicable to pathology, another medical imaging domain demanding visual diagnostic interpretation. To further characterize the detection, localization, and diagnosis of medical images, this study examined eye movements and diagnostic decision-making when pathologists were briefly exposed to digital whole slide images of melanocytic skin biopsies. Twelve resident (N = 5), fellow (N = 5), and attending pathologists (N = 2) with experience interpreting dermatopathology briefly viewed 48 cases presented for 500 ms each, and we tracked their eye movements towards histological abnormalities, their ability to classify images as containing or not containing invasive melanoma, and their ability to localize critical image regions. Results demonstrated rapid shifts of the eyes towards critical abnormalities during image viewing, high diagnostic sensitivity and specificity, and a surprisingly accurate ability to localize critical diagnostic image regions. Furthermore, when pathologists fixated critical regions with their eyes, they were subsequently much more likely to successfully localize that region on an outline of the image. Results are discussed relative to models of medical image interpretation and innovative methods for monitoring and assessing expertise development during medical education and training.

The controversy surrounding the diagnostic classification of personality disorder (PD) in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5; American Psychiatric Assoc...

ObjectiveTo investigate the value of narrow-band imaging training for differentiating between benign and malignant vocal fold leukoplakia.MethodThirty cases of vocal fold leukoplakia were selected.ResultsNarrow-band imaging endoscopy training had a significant positive effect on the specificity of the differential diagnosis of vocal fold leukoplakia. In addition, the consistency of diagnostic typing of vocal fold leukoplakia by narrow-band imaging improved to ‘moderate agreement’ following the combination of types I and II and the combination of types IV, V and VI in the typing of vocal fold leukoplakia.ConclusionThe narrow-band imaging training course may improve the ability of laryngologists to diagnose vocal fold leukoplakia. The new endoscopic diagnostic classification by narrow-band imaging needs to be further simplified to facilitate clinical application.

Introduction For the diagnosis of Parkinson's disease (PD) and atypical parkinsonism (AP) using neuroimaging, structural measures have been largely employed since structural abnormalities are most noticeable in the diseases. Functional abnormalities have been known as well, though less clearly seen, and thus, the addition of functional measures to structural measures is expected to be more informative for the diagnosis. Here, we aimed to assess whether multimodal neuroimaging measures of structural and functional alterations could have potential for enhancing performance in diverse diagnostic classification problems. Methods For 77 patients with PD, 86 patients with AP comprising multiple system atrophy and progressive supranuclear palsy, and 53 healthy controls (HC), structural and functional MRI data were collected. Gray matter (GM) volume was acquired as a structural measure, and GM regional homogeneity and degree centrality were acquired as functional measures. The measures were used as predictors individually or in combination in support vector machine classifiers for different problems of distinguishing between HC and each diagnostic type and between different diagnostic types. Results In statistical comparisons of the measures, structural alterations were extensively seen in all diagnostic types, whereas functional alterations were limited to specific diagnostic types. The addition of functional measures to the structural measure generally yielded statistically significant improvements to classification accuracy, compared to the use of the structural measure alone. Conclusion We suggest the fusion of multimodal neuroimaging measures as an effective strategy that could generally cope with diverse prediction problems of clinical concerns., The addition of functional measures (ReHo and DegCen) to the structural measure (Vol) generally yielded statistically significant improvements to classification accuracy, compared to the use of the structural measure alone, across different problems of distinguishing between healthy controls (HC) and each diagnostic type of parkinsonism (PD, MSA, and PSP) and between different diagnostic types of parkinsonism.

Rationale and Objectives To compare the ability of radiomics models including the perinodular parenchyma and standard nodular radiomics model in lung cancer diagnosis of solid pulmonary nodules smaller than 2 cm. Materials and Methods In this retrospective study, the computed tomography (CT) scans of 206 patients with a lung nodule from a single institution in 2012–2019 were collected. For each nodule, four volumes of interest were defined using the gross tumor volume (GTV) and peritumoral volumes (PTVs) of 5, 10, and 15 mm around the tumor. Results Radiomics models created from GTV, GTV plus 5 mm of PTV, GTV plus 10 mm of PTV, and GTV plus 15 mm of PTV achieved AUCs of 0.89, 0.81, 0.81, and 0.73, respectively, in the validation cohort for the diagnostic classification of benign and malignant pulmonary nodules. The performance of the models gradually decreased as the PTV increased. Wavelet features were the primary features identified in optimal radiomics signatures (2/3 in R, 4/5 in GTV plus 5 mm PTV, 3/4 in GTV plus 10 mm PTV, 2/3 in GTV plus 15 mm PTV). Conclusion Our study indicated that the radiomics signatures of GTV had a good prediction ability in distinguishing benign and malignant solid pulmonary nodules smaller than 2 cm on CT. However, the radiomics feature of the surrounding parenchyma of the nodule did not enhance the effectiveness of the diagnostic model.

This paper describes methods for automated classification of individuals by Hepatitis C medical category using data from a series of commonly used diagnostic tests. The methods are evaluated experimentally using a publicly available dataset from prior work. The accuracy of some methods compares favorably with similar results reported in prior work. In addition to quantitative results on prediction accuracy, training and testing times, and model sizes, the paper includes a detailed look at some concrete representative classifiers generated by a few of the competitive methods, permitting a human domain expert to further study the models and classifiers.

Aims Studies on the frequency of caregiver involvement in representative inpatient samples are scarce. The aim of our study was to conduct a representative survey on caregiver involvement in routine inpatient care involving all three parties (patients, caregivers, psychiatrists). Therefore, we performed face-to-face interviews consisting of open-ended questions to gain a deeper understanding of when and how caregivers are involved in care treatment and to identify which topics are mainly discussed. Methods This cross-sectional survey included inpatients from 55 acute psychiatric wards across ten psychiatric hospitals, their treating psychiatrists and, when possible, their caregivers. In total, we performed semi-structured face-to-face interviews with 247 patients, their treating psychiatrists and 94 informal caregivers. Each psychiatrist named the next two to three patients to be discharged. After a patient had given informed consent, the interview was performed by a researcher. In addition, the psychiatrist and, when possible, the primary caregiver identified by the patient, were also interviewed. Results It was perceived by both patients and psychiatrists that contact between caregiver and psychiatrist had taken place in one-third of the patient cases. Predictors for psychiatrist-caregiver-contact were revealed in the patient's diagnosis (schizophrenia), a lower history of inpatient stays, and the respective hospital. According to psychiatrists the most frequent subjects of discussion with caregivers involved therapeutic issues and organisational and social-psychiatric topics (e.g. work, living and social support). Patients and caregivers stated that psychiatric treatment and the diagnostic classification of the mental illness were the most frequent topics of conversation. For all three groups, the most often cited reason for missed caregiver involvement was the subjective perception that a caregiver was not in fact needed. Conclusions Whether or not caregivers were contacted and involved during an inpatient stay strongly depended on the individual hospital. The frequency of involvement of caregivers can certainly be increased by changing processes and structures in hospitals. All three parties (patients, caregivers and psychiatrists) most often stated that the caregiver was not involved in the treatment because they thought it was unnecessary. Evidence demonstrates the positive effect of caregivers' involvement on the therapeutic process but also on the well-being of the caregiver, therefore it is necessary to increase awareness of this evidence among all three interest groups.

ObjectivesTo understand the relevance of symptomatic hypermobility and related connective tissue variants to the expression of symptoms in Fibromyalgia and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). The study further tested if specific subfactors within the diagnostic classification of hypermobility predict clinical presentations.DesignWe report part of a larger case-control study exploring mechanisms of chronic pain and fatigue in Fibromyalgia and ME/CFS (https://doi.org/10.1186/ISRCTN78820481)Settingan NHS Clinical Research Facility.ParticipantsA subsample of 87 participants were assessed for symptomatic hypermobility by a trained clinician: 63 presented with a clinical diagnosis of either Fibromyalgia and or ME/CFS confirmed at screening; 24 participants were confirmed as healthy controls.Main outcome measures1) Brighton Criteria for joint hypermobility syndrome and 2017 hEDS diagnostic criteria. 2) ACR 2010 Diagnostic Criteria for Fibromylagia and Canadian and Fukada diagnostic criteria for ME/CFS. 3) Self report measures of subjective pain, fatigue and interoceptive sensibility.ResultsTwenty of the 63 patients (32%) presented with a clinical diagnosis of Fibromyalgia; 24 (38%) with a clinical diagnosis of ME/CFS and 19 (30%) with dual diagnoses of fibromyalgia and ME/CFS. After evaluation using clinical research tools, 56 patients (89%) met ACR diagnostic criteria for fibromyalgia; 59 (94%) Canadian Criteria for ME/CFS; and 61 (97%) Fukada Criteria for ME/CFS. After research evaluation 52 patients (85%) in fact met diagnostic criteria for Fibromyalgia and ME/CFS on all three sets of tools (ACR, Canadian, Fukada). In addition, 51 patients (81%) and 9 (37.5%) healthy controls met Brighton Criteria for joint hypermobility syndrome and 11 (18%) and 2 (8%) of patients and controls respectively, on the 2017 hEDS criteria. Of these patient participants with symptomatic hypermobility only 12 (23.5%) had received a prior diagnosis of hypermobility.Across all participants meeting Brighton Criteria, 13 (22%) also endorsed a hEDS diagnosis. Membership of the patient group was predicted by meeting the Brighton Criteria for joint hypermobility syndrome (p=ConclusionsSymptomatic joint hypermobility is relevant to symptoms and diagnosis in Fibromyalgia and ME/CFS. These conditions are poorly understood yet have a considerable impact on quality of life. Further work is needed to determine the prevalence of hEDS within the general population and define the critical clinical dimensions within symptomatic hypermobility. It is important to note the high rates of mis/underdiagnosis of symptomatic hypermobility in this group. Moreover, we need to clarify the role of variant connective tissue in dysautonomic and inflammatory mechanisms implicated in the expression of pain and fatigue in fibromyalgia and ME/CFS. Our observations have implications for diagnosis and treatment targets.Study registrationISCRTN78820481

Resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been extensively used for diagnostic classification because it does not require task compliance and is easier to pool data from multiple imaging sites, thereby increasing the sample size. A MATLAB-based toolbox called Machine Learning in NeuroImaging (MALINI) for feature extraction and disease classification is presented. The MALINI toolbox extracts functional and effective connectivity features from preprocessed rs-fMRI data and performs classification between healthy and disease groups using any of 18 popular and widely used machine learning algorithms that are based on diverse principles. A consensus classifier combining the power of multiple classifiers is also presented. The utility of the toolbox is illustrated by accompanying data consisting of resting-state functional connectivity features from healthy controls and subjects with various brain-based disorders: autism spectrum disorder from autism brain imaging data exchange (ABIDE), Alzheimer's disease and mild cognitive impairment from Alzheimer's disease neuroimaging initiative (ADNI), attention deficit hyperactivity disorder from ADHD-200, and post-traumatic stress disorder and post-concussion syndrome acquired in-house. Results of classification performed on the above datasets can be obtained from the main article titled “Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets” [1]. The data was divided into homogeneous and heterogeneous splits, such that 80% could be used for training, model building and cross-validation, while the remaining 20% of the data could be used as a hold-out independent test data for replication of the classification performance, to ensure the robustness of the classifiers to population variance in image acquisition site and age of the sample. Keywords: Resting-state functional MRI, Supervised machine learning, Diagnostic classification, Functional connectivity, Autism, ADHD, Alzheimer's disease, PTSD

Practitioners and researchers have long been challenged with identifying deceptive response styles in forensic contexts, particularly when differentiating malingering from factitious presentations. The origins and the development of factitious disorders as a diagnostic classification are discussed, as well as the many challenges and limitations present with the current diagnostic conceptualization. As an alternative to a formal diagnosis, forensic practitioners may choose to consider most factitious psychological presentations (FPPs) as a dimensional construct that are classified like malingering as a V code. Building on Rogers' central motivations for malingering, the current article provides four explanatory models for FPPs; three of these parallel malingering (pathogenic, criminological, and adaptational) but differ in their central features. In addition, the nurturance model stresses how patients with FPPs attempt to use their relationship with treating professionals to fulfill their unmet psychological needs. Relying on these models, practical guidelines are recommended for evaluating FPPs in a forensic context.

Histomorphometry of endomyocardial biopsies is one component of arrhythmogenic right ventricular cardiomyopathy (ARVC) diagnosis, although there is a need for stricter diagnostic criteria for this disease in pediatrics. The clinical utility of biopsy analysis as a component of ARVC diagnosis was evaluated in pediatric patients. Histomorphometric analysis of fibrofatty infiltrate was completed on pediatric right ventricular endomyocardial biopsy samples. Myocardial replacement by fat and fibrosis was quantified. ARVC diagnosis was established using the 2010 ARVC Task Force criteria, with the biopsy measures compared across various ARVC diagnoses (definite, borderline, possible, or no ARVC). Receiver-operating characteristic (ROC) curve analysis was also completed using biopsy measures. The greatest proportion of fat, fibrosis, and myocardial replacement was in the definite ARVC cohort, and was significantly larger than for the other diagnosis cohorts. ROC curve analysis (with the biopsy analysis removed from the diagnostic classification) produced cutoff values of 15 and 25% myocardial replacement, which is lower than current adult diagnosis criteria. We propose modifications in pediatric major and minor biopsy diagnosis criteria to allow for improved sensitivity. This study suggests that biopsy analysis in children is most significant for subjects with a more severe disease presentation.