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Research has not yet quantified the effects of workload or duty hours on the accuracy of radiologists. With the exception of a brief reduction in imaging studies during the 2020 peak of the COVID-19 pandemic, the workload of radiologists in the United States has seen relentless growth in recent years. One concern is that this increased demand could lead to reduced accuracy. Behavioral studies in species ranging from insects to humans have shown that decision speed is inversely correlated to decision accuracy. A potential solution is to institute workload and duty limits to optimize radiologist performance and patient safety. The concern, however, is that any prescribed mandated limits would be arbitrary and thus no more advantageous than allowing radiologists to self-regulate. Specific studies have been proposed to determine whether limits reduce error, and if so, to provide a principled basis for such limits. This could determine the precise susceptibility of individual radiologists to medical error as a function of speed during image viewing, the maximum number of studies that could be read during a work shift, and the appropriate shift duration as a function of time of day. Before principled recommendations for restrictions are made, however, it is important to understand how radiologists function both optimally and at the margins of adequate performance. This study examines the relationship between interpretation speed and error rates in radiology, the potential influence of artificial intelligence on reading speed and error rates, and the possible outcomes of imposed limits on both caseload and duty hours. This review concludes that the scientific evidence needed to make meaningful rules is lacking and notes that regulating workloads without scientific principles can be more harmful than not regulating at all.

ObjectivesGastric emptying testing (GET) assesses gastric motility, however is non-specific and insensitive for neuromuscular disorders. Gastric Alimetry® (GA) is a new medical device combining non-invasive gastric electrophysiological mapping and validated symptom profiling. This study assessed patient-specific phenotyping using GA compared to GET.MethodsPatients with chronic gastroduodenal symptoms underwent simultaneous GET and GA, comprising a 30-minute baseline,99mTC-labelled egg meal, and 4-hour postprandial recording. Results were referenced to normative ranges. Symptoms were profiled in the validated GA App and phenotyped using rule-based criteria based on their relationships to the meal and gastric activity: i) sensorimotor; ii) continuous; and iii) other.Results75 patients were assessed; 77% female. Motility abnormality detection rates were:GET22.7% (14 delayed, 3 rapid);GA spectral analysis33.3% (14 low rhythm stability / low amplitude; 5 high amplitude; 6 abnormal frequency);combined yield42.7%. In patients with normal spectral analysis,GA symptom phenotypesincluded: sensorimotor 17% (where symptoms strongly paired with gastric amplitude; median r=0.61); continuous 30%; other 53%. GA phenotypes showed superior correlations with GCSI, PAGI-SYM, and anxiety scales, whereas Rome IV Criteria did not correlate with psychometric scores (p>0.05).Delayed emptying was not predictive of specific GA phenotypes.ConclusionsGA improves patient phenotyping in chronic gastroduodenal disorders in the presence and absence of motility abnormalities with improved correlation with symptoms and psychometrics compared to gastric emptying status and Rome IV criteria. These findings have implications for the diagnostic profiling and personalized management of gastroduodenal disorders.Study Highlights1) WHAT IS KNOWNChronic gastroduodenal symptoms are common, costly and greatly impact on quality of lifeThere is a poor correlation between gastric emptying testing (GET) and symptomsGastric Alimetry® is a new medical device combining non-invasive gastric electrophysiological mapping and validated symptom profiling2) WHAT IS NEW HEREGastric Alimetry generates a 1.5x higher yield for motility abnormalities than GETWith symptom profiling, Gastric Alimetry identified 2.7x more specific patient categories than GETGastric Alimetry improves clinical phenotyping, with improved correlation with symptoms and psychometrics compared to GET

IntroductionBody surface gastric mapping (BSGM) is a new non-invasive test of gastric function. BSGM offers several novel and improved biomarkers of gastric function capable of differentiating patients with overlapping symptom-profiles. The aim of this study was to define normative reference intervals for BSGM spectral metrics in a population of healthy controls.MethodsBSGM was performed in healthy controls using Gastric Alimetry (Alimetry, New Zealand) comprising a stretchable high-resolution array (8×8 electrodes; 196 cm2), wearable Reader, and validated symptom-logging App. The evaluation encompassed a fasting baseline (30 min), 482 kCal meal, and 4-hr postprandial recording. Normative reference intervals were calculated for BSGM metrics including the Principal Gastric Frequency, Gastric Alimetry Rhythm Index (GA-RI; a measure of the concentration of power in the gastric frequency band over time), BMI-adjusted amplitude (µV), and fed:fasted amplitude ratio (ff-AR). Data are reported as median and reference interval (5th and/or 95th percentiles).Results110 subjects (55% female, median age 32 (IQR 24 – 50), median BMI 23.8 kg/m2 (IQR 21.4 – 26.9)) were included. The median Principal Gastric Frequency was 3.04 cpm; reference interval: 2.65 – 3.35 cpm. Median GA-RI was 0.50; reference interval: !0.25. Median BMI-adjusted amplitude was 37.6 µV; reference interval: 20-70 µV. The median ff-AR was 1.85; reference interval !1.08. Higher BMI was associated with a shorter meal-response duration (p=0.014).ConclusionsThis study provides normative reference intervals for BSGM spectral data that will be used in clinical practice to inform diagnostic interpretations of abnormal gastric function.

BackgroundElectrogastrography (EGG) non-invasively evaluates gastric function but has not achieved common clinical adoption due to several technical limitations. Body Surface Gastric Mapping (BSGM) has been introduced to overcome these limitations, but pitfalls in traditional metrics used to analyze spectral data remain unaddressed. This study critically evaluates five traditional EGG metrics and introduces improved BSGM spectral metrics, with validation in a large cohort.MethodsPitfalls in five EGG metrics were assessed (dominant frequency, percentage time normogastria, amplitude, power ratio, and instability coefficient), leading to four revised BSGM spectral metrics. Traditional and revised metrics were compared to validate performance using a 100 BSGM subject database (30 min baseline; 4-hrs postprandial), recorded using Gastric Alimetry (Alimetry, New Zealand).Key ResultsBMI and amplitude were highly correlated (r=-0.57, pBMI-adjusted amplitude metric (r=-0.21, p=0.037). Instability coefficient was highly correlated with both dominant frequency (r=-0.44, pPrincipal Gastric Frequency and Gastric Alimetry Rhythm Index (GA-RI)) that were uncorrelated (r=0.14, p=0.314). Only 28% of subjects showed a maximal averaged amplitude within the first postprandial hour. Calculating Fed:Fasted Amplitude Ratio over a 4-hr postprandial window yielded a median increase of 0.31 (IQR 0-0.64) above the traditional ratio.Conclusions & InferencesThe revised metrics resolve critical pitfalls impairing the performance of traditional EGG, and should be applied in future BSGM spectral analyses.

Chronic nausea and vomiting syndromes (NVSs) are prevalent and debilitating disorders. Putative mechanisms include gastric neuromuscular disease and dysregulation of brain-gut interaction, but clinical tests for objectively defining gastric motor function are lacking. A medical device enabling noninvasive body surface gastric mapping (BSGM) was developed and applied to evaluate NVS pathophysiology. BSGM was performed in 43 patients with NVS and 43 matched controls using Gastric Alimetry (Alimetry), a conformable high-resolution array (8 × 8 electrodes; 20-mm interelectrode spacing), wearable reader, and validated symptom-logging app. Continuous measurement encompassed a fasting baseline (30 minutes), 482-kilocalorie meal, and 4-hour postprandial recording, followed by spectral and spatial biomarker analyses. Meal responses were impaired in NVS, with reduced amplitudes compared to controls (median, 23.3 microvolts versus 38.0 microvolts, P < 0.001), impaired fed-fasting power ratios (1.1 versus 1.6, P = 0.02), and disorganized slow waves (spatial frequency stability, 13.6 versus 49.5; P < 0.001). Two distinct NVS subgroups were evident with indistinguishable symptoms (all P > 0.05). Most patients (62%) had normal BSGM studies with increased psychological comorbidities (43.5% versus 7.7%; P = 0.03) and anxiety scores (median, 16.5 versus 13.0; P = 0.035). A smaller subgroup (31%) had markedly abnormal BSGM, with biomarkers correlating with symptoms (nausea, pain, excessive fullness, early satiety, and bloating; all r > 0.35, P < 0.05). Patients with NVS share overlapping symptoms but comprise distinct underlying phenotypes as revealed by a BSGM device. These phenotypes correlate with symptoms, which should inform clinical management and therapeutic trial design.

ObjectiveTo define phenotypes of gastric myoelectrical abnormalities and relation to symptoms in people with longstanding T1D, compared to matched healthy controls, using a novel non-invasive body surface gastric mapping (BSGM) device.Research design and methodsBSGM was performed on people with T1D of >10 years duration and matched controls, employing Gastric Alimetry (Alimetry, New Zealand), comprising a high-resolution 64-channel array, validated symptom logging App, and wearable reader.Results32 people with T1D were recruited (15 with a high symptom burden), and 32 controls. Those with symptoms showed more unstable gastric myoelectrical activity, (Gastric Alimetry Rhythm Index 0.39 vs 0.51, p=0.017; and lower average spatial covariance 0.48 vs 0.51, p=0.009) compared with controls. Those with T1D and symptoms also had higher prevalence of peripheral neuropathy (67% vs 6%, p=0.001), anxiety/depression diagnoses (27% vs 0%, p=0.001), and mean HbA1c levels (76 vs 56 mmol/mol, p0.35, pConclusionGastroduodenal symptoms in people with longstanding T1D correlate with gastric myoelectrical abnormalities on BSGM evaluation, in addition to glycemic control, psychological comorbidities, and peripheral neuropathy. BSGM using the Gastric Alimetry device identified a range of myoelectrical phenotypes, representing both myogenic and neurogenic mechanisms, which represent targets for diagnosis, monitoring and therapy.

It may seem unlikely that the field of radiology perpetuates disparities in health care, as most radiologists never interact directly with patients, and racial bias is not an obvious factor when interpreting images. However, a closer look reveals that imaging plays an important role in the propagation of disparities. For example, many advanced and resource-intensive imaging modalities, such as MRI and PET/CT, are generally less available in the hospitals frequented by people of color, and when they are available, access is impeded due to longer travel and wait times. Furthermore, their images may be of lower quality, and their interpretations may be more error prone. The aggregate effect of these imaging acquisition and interpretation disparities in conjunction with social factors is insufficiently recognized as part of the wide variation in disease outcomes seen between races in America. Understanding the nature of disparities in radiology is important to effectively deploy the resources and expertise necessary to mitigate disparities through diversity and inclusion efforts, research, and advocacy. In this article, the authors discuss disparities in access to imaging, examine their causes, and propose solutions aimed at addressing these disparities.

Purpose In this study, the authors applied evidence-based medicine to decrease the utilization of routine chest radiography in adult intensive care units and used time-driven activity-based costing to demonstrate cost savings. Methods A multidisciplinary team was formed with representatives from radiology, surgery, internal medicine, and nursing. The process of performing a portable chest radiographic examination was mapped, and time trials were performed by the radiology technologists and radiology resident. This information was used to determine the cost of performing portable intensive care unit (ICU) chest radiographic studies. The clinical team changed resident education, ordering protocols, and workflows to discontinue the use of routine daily chest radiography, emphasizing that it should be ordered only in specific situations, such as on admission or after central line placement. In addition, as a balancing measure, the team tracked complications such as unplanned extubations and ventilator days. Results Changing ordering practices in the adult ICUs to align with established evidence-based guidelines resulted in a 37% decrease in the utilization of portable chest radiography between June and December, without a concomitant increase in unplanned extubations or ventilator days. In addition, a proportionate cost savings was realized, as demonstrated by the application of time-driven activity-based costing. Conclusions This performance improvement initiative successfully increased the value of care delivered to ICU patients by aligning institutional clinical practice with evidence-based medicine. This resulted in decreased utilization and the cost associated with delivering care without a concomitant increase in complications., Graphical abstract

Body surface gastric mapping (BSGM) is a new clinical tool for gastric motility diagnostics, providing high-resolution data on gastric myoelectrical activity. Artifact contamination was a key challenge to reliable test interpretation in traditional electrogastrography. This study aimed to introduce and validate an automated artifact detection and rejection system for clinical BSGM applications.Ten patients with chronic gastric symptoms generated a variety of artifacts according to a standardized protocol (176 recordings) using a commercial BSGM system (Alimetry, New Zealand). An automated artifact detection and rejection algorithm was developed, and its performance was compared with a reference standard comprising consensus labeling by 3 analysis experts, followed by comparison with 6 clinicians (3 untrained and 3 trained in artifact detection). Inter-rater reliability was calculated using Fleiss' kappa.Inter-rater reliability was 0.84 (95% CI:0.77-0.90) among experts, 0.76 (95% CI:0.68-0.83) among untrained clinicians, and 0.71 (95% CI:0.62-0.79) among trained clinicians. The sensitivity and specificity of the algorithm against experts was 96% (95% CI:91%-100%) and 95% (95% CI:90%-99%), respectively, vs 77% (95% CI:68%-85%) and 99% (95% CI:96%-100%) against untrained clinicians, and 97% (95% CI:92%-100%) and 88% (95% CI:82%-94%) against trained clinicians.An automated artifact detection and rejection algorithm was developed showinggt;95% sensitivity and specificity vs expert markers. This algorithm overcomes an important challenge in the clinical translation of BSGM and is now being routinely implemented in patient test interpretations.

Disorders of gastric function are highly prevalent, but diagnosis often remains symptom-based and inconclusive. Body surface gastric mapping is an emerging diagnostic solution, but current approaches lack scalability and are cumbersome and clinically impractical. We present a novel scalable system for non-invasively mapping gastric electrophysiology in high-resolution (HR) at the body surface.The system comprises a custom-designed stretchable high-resolution "peel-and-stick" sensor array (8 × 8 pre-gelled Ag/AgCl electrodes at 2 cm spacing; area 225 cmGastric mapping was successfully achieved non-invasively in all cases (16 male; 8 female; aged 20-73 years; BMI 24.2 ± 3.5). In all subjects, gastric electrophysiology and meal responses were successfully captured and quantified non-invasively (mean frequency 2.9 ± 0.3 cycles per minute; peak amplitude at mean 60 m postprandially with return to baseline in4 h). Spatiotemporal mapping showed regular and consistent wave activity of mean direction 182.7° ± 73 (74.7% antegrade, 7.8% retrograde, 17.5% indeterminate).BSGM is a new diagnostic tool for assessing gastric function that is scalable and ready for clinical applications, offering several biomarkers that are improved or new to gastroenterology practice.

Fluoroscopic-guided lumbar puncture (LP) is a procedure commonly performed by radiologists, which in some circumstances may be difficult or impossible using a traditional posterior interspinous or interlaminar approach. Alternatives to LP include cervical and cisternal punctures, placement of an Ommaya reservoir, and lumbar laminectomy. More recently, however, there has been a move toward access of the thecal sac through a transforaminal approach in patients with challenging anatomy. This report outlines our approach and experience using transforaminal LP (TFLP) in patients with spinal muscular atrophy (SMA) with a 100% success rate. We discuss its utility in other patients with difficult access and compare TFLP with other techniques to access the intrathecal space.

As the first step in image interpretation is detection, an error in perception can prematurely end the diagnostic process leading to missed diagnoses. Because perceptual errors of this sort-"failure to detect"-are the most common interpretive error (and cause of litigation) in radiology, understanding the nature of perceptual expertise is essential in decreasing radiology's long-standing error rates. In this article, we review what constitutes a perceptual error, the existing models of radiologic image perception, the development of perceptual expertise and how it can be tested, perceptual learning methods in training radiologists, and why understanding perceptual expertise is still relevant in the era of artificial intelligence. Adding targeted interventions, such as perceptual learning, to existing teaching practices, has the potential to enhance expertise and reduce medical error.

ImportanceChronic nausea and vomiting syndromes (NVS) are prevalent and debilitating disorders. Putative mechanisms include gastric neuromuscular disease and dysregulation of brain-gut interaction, but clinical tests for objectively defining gastric motor function are lacking.ObjectiveA novel medical device enabling non-invasive body surface gastric mapping (BSGM) was developed and applied to evaluate NVS pathophysiology.DesignA case-control study where BSGM was performed in NVS patients and matched controls using Gastric Alimetry (Alimetry, New Zealand), comprising a conformable high-resolution array (8×8 electrodes; 20 mm inter-electrode spacing), wearable Reader, and validated symptom logging App. Continuous measurement encompassed a fasting baseline (30 min), 482 kCal meal (10 min), and 4-hr post-prandial recording.SettingMulticenter study in Auckland, New Zealand and Calgary, Canada.Participants43 NVS patients (gastroparesis and Rome IV chronic NVS) and 43 matched controls.Main outcomes and measuresSymptom severity and quality of life were measured using Patient Assessment of Upper Gastrointestinal Disorders-Symptom Severity Index (PAGI-SYM), Gastroparesis Cardinal Symptom Index (GCSI), and Patient Assessment of Upper Gastrointestinal Disorders-Quality of Life (PAGI-QOL) instruments. Health psychology metrics included the State Trait Anxiety Inventory Short Form (STAI-SF) and Patient Health Questionnaire-2 (PHQ-2) questionnaires. Spectral analyses including frequency, amplitude, and fed-fasting power ratio. Spatial biomarker analyses included spatial frequency stability and average spatial covariance.ResultsMeal responses were impaired in NVS, with reduced amplitudes compared to controls (median 23.3 vs 38.0 µV, p0.05). A majority (62%) had normal BSGM studies (all biomarkers non-significant vs controls) with increased psychological comorbidities (43.5% vs 7.7%; p=0.03) and anxiety scores (median 16.5 vs 13.0; p=0.035). A smaller subgroup (31%) had markedly abnormal BSGM, with test biomarkers correlating with symptoms (nausea, pain, excessive fullness, early satiety, bloating; all r>0.35, pConclusions and RelevanceNVS patients share overlapping symptoms, but comprise distinct underlying phenotypes as revealed by a novel BSGM device. These phenotypes correlate with symptoms, which should inform clinical management and allocations into therapeutic trials.Key PointsQuestionHow does body surface gastric mapping, a novel non-invasive medical device for evaluating gastric motility, aid assessment of patients with chronic nausea and vomiting.FindingsTwo subgroups were revealed in chronic nausea and vomiting syndromes, which could not be differentiated by symptoms alone. Where body surface gastric mapping was normal, symptoms correlated with psychological comorbidities, and where body surface gastric mapping was abnormal, symptoms correlated with gastric electrophysiology metrics.MeaningDistinct phenotypes revealed by body surface gastric mapping correlate with symptoms, which should inform targeted clinical management and allocations into therapeutic trials.

Disorders of gastric function are highly prevalent, but diagnosis often remains symptom-based and inconclusive. Body surface gastric mapping is an emerging diagnostic solution, but current approaches lack scalability and are cumbersome and clinically impractical. We present a novel scalable system for non-invasively mapping gastric electrophysiology in high-resolution (HR) at the body-surface. The system comprises a custom-designed flexible HR sensor array and portable data-logger synchronized to an App, with automated analysis and visualization algorithms. The novel system underwent performance testing then validation in 24 healthy subjects. In all subjects, gastric electrophysiology and meal responses were successfully captured and mapped non-invasively (mean frequency 2.9 ± 0.3 cycles per minute; peak amplitude at mean 60 m postprandially with return to baseline in

BackgroundFunctional gastroduodenal disorders include functional dyspepsia, chronic nausea and vomiting syndromes, and gastroparesis. These disorders are common, but their overlapping symptomatology poses challenges to diagnosis, research, and therapy. This study aimed to introduce and validate a standardized patient symptom-logging system and App to aid in the accurate reporting of gastroduodenal symptoms for clinical and research applications.MethodsThe system was implemented in an iOS App including pictographic symptom illustrations, and two validation studies were conducted. To assess convergent and concurrent validity, a diverse cohort with chronic gastroduodenal symptoms undertook App-based symptom logging for 4-hours after a test meal. Individual and total post-prandial symptom scores were averaged and correlated against two previously validated instruments: PAGI-SYM (for convergent validity) and PAGI-QOL (for concurrent validity). To assess face and content validity, semi-structured qualitative interviews were conducted with patients.Key ResultsApp-based symptom reporting demonstrated robust convergent validity with PAGI-SYM measures of nausea (rS=0.68), early satiation (rS=0.55), bloating (rS=0.48), heartburn (rS=0.47), upper gut pain (rS=0.40) and excessive fullness (rS=0.40); all p.001 (n=79). The total App-reported Gastric Symptom Burden Score correlated positively with PAGI-SYM (rS=0.56; convergent validity; p.001), and negatively with PAGI-QOL (rS=-0.34; concurrent validity; p=0.002). Interviews demonstrated that the pictograms had adequate face and content validity.Conclusions and InferencesThe continuous patient symptom-logging App demonstrated robust convergent, concurrent, face, and content validity when used within a 4-hour post-prandial test protocol. The App will enable standardized symptom reporting and is anticipated to provide utility in both research and clinical practice.

Gastric disorders are increasingly prevalent, but reliable clinical tools to objectively assess gastric function are lacking. Body-surface gastric mapping (BSGM) is a non-invasive method for the detection of gastric electrophysiological biomarkers including slow wave direction, which have correlated with symptoms in patients with gastroparesis and functional dyspepsia. However, no studies have validated the relationship between gastric slow waves and body surface activation profiles. This study aimed to comprehensively evaluate the relationship between gastric slow waves and body-surface recordings. High-resolution electrode arrays were placed to simultaneously capture slow waves from the gastric serosa (32×6 electrodes at 4 mm resolution) and abdominal surface (8×8 at 20 mm inter-electrode spacing) in a porcine model. BSGM signals were extracted based on a combination of wavelet and phase information analyses. A total of 1185 individual cycles of slow waves assessed, out of which 897 (76%) were normal antegrade waves, occurring in 10/14 (71%) subjects studied. BSGM accurately detected the underlying slow wave in terms of frequency (r = 0.99, p = 0.43) as well as the direction of propagation (p = 0.41, F-measure: 0.92). In addition, the cycle-by-cycle match between BSGM and transitions of gastric slow waves in terms either or both temporal and spatial abnormalities was demonstrated. These results validate BSGM as a suitable method for non-invasively and accurately detecting gastric slow wave activation profiles from the body surface.Single sentence summarySimultaneous recordings of the stomach using serosal and body-surface electrode arrays demonstrated reliable detection of frequency and classification of propagation.

The business case for employee retention is well established in the literature. Simply stated, it is more financially advantageous to keep employees than to recruit and hire someone new. Recent studies have estimated the cost of turnover due to physician burnout in the United States at approximately $4.3 billion annually. The question remains, What are the best strategies to retain employees and keep them engaged? This article addresses the critical issues of employee turnover, delineates the reasons that employees leave, characterizes employees at risk of turnover, and describes retention strategies that overlap with strategies that address burnout and disengagement.

Expert witnesses provide an important service in malpractice cases in the United States because they educate the jury on the standards of care relevant to a particular case. In cases in which the defendant physician is a radiologist, the decision often rests on whether a retrospectively detected abnormality should have been perceived and reported, an "error of omission." Errors of omission are usually termed "perceptual" in the literature and are the most common cause of malpractice suits in radiology. Allegations often hinge on whether these errors represent a breach of duty by the defendant radiologist and whether they resulted in an injury to the plaintiff or patient. In short, jurors are asked to decide if the radiologist performed below the "standard of care," generally defined as that which a minimally competent, reasonable, or ordinary physician in the same field would do under similar circumstances. The authors describe challenges associated with being an expert witness and provide guidance to radiologists on how to address cases involving alleged perceptual errors.

Commonly conflated with sleepiness, fatigue is a distinct multidimensional condition with physical and mental effects. Fatigue in health care providers and any secondary effects on patient care are an important societal concern. As medical image interpretation is highly dependent on visual input, visual fatigue is of particular interest to radiologists. Humans analyze their surroundings with rapid eye movements called saccades, and fatigue decreases saccadic velocity. Oculomotor parameters may, therefore, be an objective and reproducible metric of fatigue and eye movement analysis can provide valuable insight into the etiology of fatigue-related error.

Conflicting reports exist regarding the racial and the gender distribution of rheumatoid arthritis-related interstitial lung disease (RA-ILD). In a major population study of predominately Whites, RA-ILD was reported mainly among smoker middle-aged men. However, recent data suggest that the disease is that of elderly women. Our study aimed to assess the prevalence and identify the gender differences and clinical characteristics of RA-ILD in a predominantly Black population.Cross-sectional analysis of data obtained from the records of 1142 patients with RA diagnosis by ICD codes of which 503 cases met the inclusion criteria for the study. Eighty-six patients had chronic respiratory symptoms of cough and dyspnea and were further assessed by our multidisciplinary group of investigators. Thirty-two subjects with an established diagnosis of rheumatoid arthritis met the diagnostic criteria for interstitial lung disease.Of the 32 patients with RA-ILD, mean age was 62.6 ± 2.2 (± SEM), 93.7% were females, and 89% Blacks with a BMI = 29.2 (Kg/mOur study indicates RA-ILD among Blacks is predominantly a disease of elderly females with higher rates of GERD and CVD risk factors. Further studies are needed to identify the pathogenetic differences accounting for the gender distribution of RA-ILD among Black and White populations.Key Points• First study to assess ILD among predominantly Black RA patients.• The prevalence of RA-associated ILD was 6.36%, affecting mostly women in their sixth decade with seropositive disease.• COPD was the most common airway disease among non-RA-ILD Black population.• GERD was found in approximately one-third of patients with RA-associated ILD versus one-fifth of those RA patients without any lung disease.

Rheumatoid arthritis (RA) patients have nearly twice the risk of cardiovascular disease (CVD) compared to the general population. We aimed to assess, in a predominantly Black population, the prevalence of traditional and RA-specific CVD risk factors and therapeutic patterns. Utilizing ICD codes, we identified 503 RA patients ≥18 years old who were seen from 2010 to 2017. Of them, 88.5% were Black, 87.9% were women and 29.4% were smokers. CVD risk factors (obesity, diabetes, hypertension, dyslipidemia) were higher than in previously reported White RA cohorts. Eighty-seven percent of the patients had at least one traditional CVD risk factor, 37% had three or more traditional CVD risk factors and 58% had RA-specific risk factors (seropositive RA, &gt, 10 years of disease, joint erosions, elevated inflammatory markers, extra-articular disease, body mass index (BMI) &lt, 20). CV outcomes (coronary artery disease/myocardial infarction, heart failure, atrial fibrillation and stroke) were comparable to published reports. Higher steroid use, which increases CVD risk, and lesser utilization of biologics (decrease CV risk) were also observed. Our Black RA cohort had higher rates of traditional CVD risk factors, in addition to chronic inflammation from aggressive RA, which places our patients at a higher risk for CVD outcomes, calling for revised risk stratification strategies and effective interventions to address comorbidities in this vulnerable population.

Supported by guidance from training during residency programs, radiologists learn clinically relevant visual features by viewing thousands of medical images. Yet the precise visual features that expert radiologists use in their clinical practice remain unknown. Identifying such features would allow the development of perceptual learning training methods targeted to the optimization of radiology training and the reduction of medical error. Here we review attempts to bridge current gaps in understanding with a focus on computational saliency models that characterize and predict gaze behavior in radiologists. There have been great strides toward the accurate prediction of relevant medical information within images, thereby facilitating the development of novel computer-aided detection and diagnostic tools. In some cases, computational models have achieved equivalent sensitivity to that of radiologists, suggesting that we may be close to identifying the underlying visual representations that radiologists use. However, because the relevant bottom-up features vary across task context and imaging modalities, it will also be necessary to identify relevant top-down factors before perceptual expertise in radiology can be fully understood. Progress along these dimensions will improve the tools available for educating new generations of radiologists, and aid in the detection of medically relevant information, ultimately improving patient health.

This article reviews examples of easily missed, subtle, or ambiguous lesions on chest radiography. Anecdotally, reliance on cross-sectional imaging has diminished radiologists׳ comfort level with chest radiograph interpretation. However, plain film chest radiography remains a valuable tool in diagnostic imaging. It is noninvasive, inexpensive, easily obtained, and diagnostic for many common conditions with the benefit of a very low radiation dose. It is one of the most commonly ordered radiographic examinations; therefore, proficiency in chest radiograph interpretation remains an essential tool in our diagnostic armamentarium. Certain anatomical regions on chest radiographs are particularly prone to perceptual errors. These are often referred to as "blind spots" and have been emphasized as we demonstrate methods that can be applied to search patterns to improve detection of abnormalities.

Communication failures are a common cause of patient harm and malpractice claims against radiologists. In addition to overt communication breakdowns among providers, it is also important to address the quality of communication to optimize patient outcomes. In this review, we describe common communication failures and potential solutions providing a framework for radiologists to improve health care delivery.

Interpretive errors in diagnostic imaging result in significant patient morbidity and mortality, but the importance of errors and process failures in the imaging cycle other than during image interpretation is underappreciated. In this article, we describe these errors and potential solutions, providing a framework to improve patient safety and understand the changing roles of radiologists beyond image interpretation.For comprehensive improvements to health care delivery, other failures in the cycle besides diagnostic interpretive error-such as ordering inappropriate studies, PACS failures, and a lack of accurate clinician contact information (with resultant communication failure)-should be recognized as contributors to patient harm because they lead to wasted resources and delayed care. By taking ownership of the entire imaging cycle, radiologists can increase their net worth to patient care and cement their roles as experts in the effective, evidence-based use of imaging technologies.

Over the past decade, software companies have increasingly monopolized the flow of venture capital, starving support for scientific research and its transformative discoveries. New medicines, cheaper and faster personal computers, and other life-changing developments all stem from investment in science. In the past, these funds led to steam engines, light bulbs, microprocessors, 3D printers, and even the Internet. In Venture Investing in Science, the venture capitalist Douglas W. Jamison and the investment author Stephen R. Waite directly link financial support to revolutionary advancements in physics, computers, chemistry, and biology and make a passionate case for continued investing in science to meet the global challenges of our time. Clean air and water, cures for intractable diseases, greener public transportation, cheaper and faster communication technologies—these are some of the rich opportunities awaiting venture capital investment today. Jamison and Waite focus on how early-stage companies specializing in commercializing transformative technologies based on deep science have been shunned by venture capitalists, and how the development of such companies have been hampered by structural changes in capital markets and government regulation over the past decade. The authors argue that reinvigorating science-based technological innovation is crucial to reactivating the economic dynamism that lifts living standards and fuels prosperity over time.

Roost location is a key factor affecting the survival and fitness of British bats. It has been suggested that a knowledge and understanding of the factors which may influence the selection of roost location are fundamental to conservation efforts. Our study aims to investigate the relationship between Eptesicus serotinus roost location and landscape structure. The study is based in the Sussex region of South East England. The landscape characteristics of 97 roosts locations were compared against 100 random control locations. Habitat analysis was carried out at three distance bands and included an analysis of roost density. The results indicate that E. serotinus is selective in locating roosts. The study demonstrates that there are significant differences between the landscape composition surrounding roost sites and the wider landscape. In particular, E. serotinus roost sites are found to be located in areas with a significantly higher cover of arable land and improved grassland. Kernel density analysis was successfully used as an additional method to the direct comparison of roost neighbourhood composition. Density analysis identified the location and characteristics of possible centres of E. serotinus activity. It is anticipated that the findings will enable the needs of bats to be considered in future landscape conservation initiatives and development policies.

Although imaging technology has advanced significantly since the work of Garland in 1949, interpretive error rates remain unchanged. In addition to patient harm, interpretive errors are a major cause of litigation and distress to radiologists. In this article, we discuss the mechanics involved in searching an image, categorize omission errors, and discuss factors influencing diagnostic accuracy. Potential individual- and system-based solutions to mitigate or eliminate errors are also discussed.Radiologists use visual detection, pattern recognition, memory, and cognitive reasoning to synthesize final interpretations of radiologic studies. This synthesis is performed in an environment in which there are numerous extrinsic distractors, increasing workloads and fatigue. Given the ultimately human task of perception, some degree of error is likely inevitable even with experienced observers. However, an understanding of the causes of interpretive errors can help in the development of tools to mitigate errors and improve patient safety.

Carnivore survey protocols that properly address spatial sampling and detectability issues are seldom feasible at a landscape-scale. This limits knowledge of large-scale patterns in distribution, abundance and their underlying determinants, hindering conservation of globally threatened carnivore populations. Occupancy analysis of data from logistically efficient sign surveys along consecutive road segments (spatially auto-correlated replicates) offers a potential solution. We adapted and applied this newly-developed method over 62,979 km 2 of human-modified land in South Africa. Our aims were to (1) generate unbiased estimates of brown hyaena occupancy and abundance (2) investigate two suspected determinants of occupancy using a combination of biological and socio-economic sampling techniques, and (3) use simulations to evaluate the effort required for abundance and occupancy estimates with acceptable bias, precision and power. Brown hyaena occupancy was estimated at 0.748 (±SE 0.1), and estimated overall density in agricultural land (0.15/100 km 2 , ±SE 0.08) was an order of magnitude lower than in protected areas. Positive attitudes to carnivores and presence of wildlife farms exerted strong positive effects on occupancy, so changes in these factors may well exert monotonic impacts on local metapopulation status. Producing reliable occupancy and abundance estimates would require ⩾6 replicates and ⩾12 replicates per site respectively. Detecting 50% and 30% declines in brown hyaena occupancy with adequate power would require five annual surveys at ⩾65 sites and ⩾125 sites respectively. Our results suggest that protocols based on spatially auto-correlated sign survey replicates could be used to monitor carnivore populations at large, and possibly even country-wide spatial scales.