Search

Computational thinking (CT) is an important 21st-century skill. This paper aims at investigating predictors of CT self-efficacy among high-school students. The hypothesized predictors are grouped into three areas: (1) student characteristics, (2) home environment, and (3) learning opportunities. CT self-efficacy is measured with the Computational Thinking Scales (CTS) that comprises five dimensions: creativity, algorithmic thinking, cooperativity, critical thinking, and problem solving. N = 202 high-school students act as the sample, linear regression as the analysis method. The best prediction is possible for algorithmic thinking (R[superscript 2] = 0.511). For cooperativity, the explanatory power of our model it is weak (R[superscript 2] = 0.146). Across all five CTS dimensions, CT self-concept is the best predictor for CT self-efficacy. [For the full proceedings, see ED636095.]

Digital data and algorithms have over the past years increasingly found their way into law enforcement contexts, including modes of biometric identification and matching, automated surveillance capacities, short-term situational predictions, AI-supported analysis for large amounts of data, and the interoperability of large-scale databases and platforms for data exchange and investigation. These tools can help to increase the effectiveness and efficiency of law enforcement operations on the strategic, tactical, and operational level. They do, however, also come with a number of concerns that must be acknowledged and addressed in order to realize their potential and avoid unintended side-effects and societal frictions. Based on a multi-year research project on predictive policing in Germany and Switzerland, this paper provides a perspective on some of the challenges involved in implementing new and emerging technologies in law enforcement contexts. Specifically, it addresses (1) the nature of data, i.e. how data are socially constructed and present a particular account of the world, inevitably leading to "biased" results; (2) transparency in algorithms and AI, i.e. how "black boxes" undercut human capacities to understand and retrace processes and create problems for public accountability; (3) automation and human control, i.e. the question how human operators can retain meaningful influence over analytical processes; (4) decision-making processes and automation bias, i.e. how humans can be empowered to critically question and override system recommendations; and (5) strategic and societal implications, i.e. the fact that digital tools should not be misused to displace larger programs that address the root causes of crime. [ABSTRACT FROM AUTHOR]

Microwave radiometers are widely used for the retrieval of Liquid Water Path (LWP) and Integrated Water Vapor (IWV) in the context of cloud and precipitation studies. This paper presents a new site-independent retrieval algorithm for LWP and IWV, relying on a single-frequency 89-GHz ground-based radiometer. A statistical approach is used, based on a neural network, which is trained and tested on a synthetic data set constructed from radiosonde profiles worldwide. In addition to 89-GHz brightness temperature, the input features include surface measurements of temperature, pressure and humidity, as well as geographical information and, when available, estimates of IWV and LWP from reanalysis data. An analysis of the algorithm is presented to assess its accuracy, the impact of the various input features, as well as its sensitivity to radiometer calibration and its stability across geographical locations. The new method is then implemented on real data that were collected during a field deployment in Switzerland and during the ICE-POP 2018 campaign in South Korea. The new algorithm is shown to be quite robust, especially in mid-latitude environments with a moderately moist climate, although its accuracy is inevitably lower than that obtained with state-of-the-art multi-channel radiometers. [ABSTRACT FROM AUTHOR]

Background: Ulcus Vulvae Acutum Lipschütz (UVAL) is a largely unknown disease with a broad and complex differential diagnosis. Objectives: To provide a description of the main characteristics of UVAL, determine the most appropriate diagnostic process and describe the current therapeutic approach. Methods: We designed a retrospective, descriptive cohort study using the gynecological-ER database of our institution. Inclusion criteria: female patients aged between 10 and 20 years old with suspicion of a UVAL diagnosis at CHUV's gynecological ER. Data extraction: epidemiological characteristics, clinical presentation, laboratory tests, established diagnostics, treatment, and ulcer outcomes. Results: 15 patients were included for the analysis; average age: 15 years old; 60% of patients were virgo at the time of ulcer onset; all patients had at least one flu-like symptom concomitant with the vulvar lesion; the most-performed serology was for EBV and acute disease was present in only one patient; for diagnostic purposes two biopsies were performed with both inconclusive histopathology analysis; the main prescribed treatments were: oral NSAIDs, Paracetamol, and Lidocaine gel; 93% of cases presented signs of regression; the average follow-up time was 10 days. The diagnostic algorithm of Sadoghi et al: 10 out of 15 cases were retrospectively diagnosed with UVAL by the algorithm; half were diagnosed with UVAL, and the other half received a diagnosis of "ulcers of unknown origin" at the time of the gynecological ER visit. Conclusions: We highly recommend the diagnostic and therapeutic algorithms developed by Sadoghi et al. as valuable tools to guide clinical reasoning and, consequently, improve acute vulvar ulcers management. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models, especially deep neural networks, are increasingly being used for the analysis of medical images and as a supporting tool for clinical decision-making. In this study, we propose an artificial intelligence system to facilitate dental decision-making for the removal of mandibular third molars (M3M) based on 2-dimensional orthopantograms and the risk assessment of such a procedure. A total of 4,516 panoramic radiographic images collected at the Center of Dental Medicine at the University of Zurich, Switzerland, were used for training the ML model. After image preparation and preprocessing, a spatially dependent U-Net was employed to detect and retrieve the region of the M3M and inferior alveolar nerve (IAN). Image patches identified to contain a M3M were automatically processed by a deep neural network for the classification of M3M superimposition over the IAN (task 1) and M3M root development (task 2). A control evaluation set of 120 images, collected from a different data source than the training data and labeled by 5 dental practitioners, was leveraged to reliably evaluate model performance. By 10-fold cross-validation, we achieved accuracy values of 0.94 and 0.93 for the M3M–IAN superimposition task and the M3M root development task, respectively, and accuracies of 0.9 and 0.87 when evaluated on the control data set, using a ResNet-101 trained in a semisupervised fashion. Matthew's correlation coefficient values of 0.82 and 0.75 for task 1 and task 2, evaluated on the control data set, indicate robust generalization of our model. Depending on the different label combinations of task 1 and task 2, we propose a diagnostic table that suggests whether additional imaging via 3-dimensional cone beam tomography is advisable. Ultimately, computer-aided decision-making tools benefit clinical practice by enabling efficient and risk-reduced decision-making and by supporting less experienced practitioners before the surgical removal of the M3M. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of introducing external medical review for disability insurance (DI) in a system relying on treating physician testimony for eligibility determination. Using a unique policy change and administrative data from Switzerland, I show that medical review reduces DI incidence by 23%. Incidence reductions are closely tied to difficult-to-diagnose conditions, suggesting inaccurate assessments by treating physicians. Due to a partial benefit system, reductions in full benefit awards are partly offset by increases in partial benefits. More intense screening also increases labor market participation. Existing benefit recipients are downgraded and lose part of their benefit income when scheduled medical reviews occur. Back-of-the-envelope calculations indicate that external medical review is highly cost-effective. Under additional assumptions, the results provide a lower bound of the effect on the false positive award error rate. [ABSTRACT FROM AUTHOR]

This article presents a calibration transfer methodology that can be used between radars of the same or different frequency bands. This method enables the absolute calibration of a cloud radar by transferring it from another collocated instrument with known calibration, by simultaneously measuring vertical ice cloud reflectivity profiles. The advantage is that the added uncertainty in the newly calibrated instrument can converge to the magnitude of the reference instrument calibration. This is achieved by carefully selecting comparable data, including the identification of the reflectivity range that avoids the disparities introduced by differences in sensitivity or scattering regime. The result is a correction coefficient used to compensate measurement bias in the uncalibrated instrument. Calibration transfer uncertainty can be reduced by increasing the number of sampling periods. The methodology was applied between collocated W-band radars deployed during the ICE-GENESIS campaign (Switzerland 2020/21). A difference of 2.2 dB was found in their reflectivity measurements, with an uncertainty of 0.7 dB. The calibration transfer was also applied to radars of different frequency, an X-band radar with unknown calibration and a W-band radar with manufacturer calibration; the difference found was −16.7 dB with an uncertainty of 1.2 dB. The method was validated through closure, by transferring calibration between three different radars in two different case studies. For the first case, involving three W-band radars, the bias found was of 0.2 dB. In the second case, involving two W-band and one X-band radar, the bias found was of 0.3 dB. These results imply that the biases introduced by performing the calibration transfer with this method are negligible. [ABSTRACT FROM AUTHOR]

In the digital age, the use of advanced technology is becoming a new paradigm in police work, criminal justice, and the penal system. Algorithms promise to predict delinquent behaviour, identify potentially dangerous persons, and support crime investigation. Algorithm-based applications are often deployed in this context, laying the groundwork for a 'smart criminal justice'. In this qualitative study based on 32 interviews with criminal justice and police officials, we explore the reasons why and extent to which such a smart criminal justice system has already been established in Switzerland, and the benefits perceived by users. Drawing upon this research, we address the spread, application, technical background, institutional implementation, and psychological aspects of the use of algorithms in the criminal justice system. We find that the Swiss criminal justice system is already significantly shaped by algorithms, a change motivated by political expectations and demands for efficiency. Until now, algorithms have only been used at a low level of automation and technical complexity and the levels of benefit perceived vary. This study also identifies the need for critical evaluation and research-based optimization of the implementation of advanced technology. Societal implications, as well as the legal foundations of the use of algorithms, are often insufficiently taken into account. By discussing the main challenges to and issues with algorithm use in this field, this work lays the foundation for further research and debate regarding how to guarantee that 'smart' criminal justice is actually carried out smartly. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the availability and use of dental and maxillofacial emergency algorithms in Swiss hospitals. A survey was performed among physicians at Swiss emergency departments (ED) and participants of the "36th Annual Meeting of the Society for Oral and Cranio-Maxillofacial Surgery". Eighty-nine EDs in Switzerland were questioned about the availability and use of electronic algorithms in their hospitals. Eighty-one (91%) participated in the study. In 75 (93%) of the EDs, electronic algorithms are used, mainly "medStandards". Six have no available algorithms. Fifty-two (64%) use algorithms daily. Eight (10%) Swiss EDs have maxillofacial and dental algorithms, and 73 (90%) have no access to or do not know about them. For dental algorithms, 28 (38%) of the respondents would like to have access, and 16 (22%) do not desire access. For maxillofacial algorithms, 23 (32%) want to have access and 21 (29%) do not want it. Most (74%) of the participating maxillofacial surgeons did not know about the existence of ED algorithms regarding their specialty. Our study shows that the existence of specific algorithms is often not known. Furthermore, there is a demand for dental and maxillofacial algorithms in Swiss EDs. [ABSTRACT FROM AUTHOR]

Background Studies estimate that approximately one-third of episodes of delirium are preventable and that delirium prevention and management are often suboptimal in practice. While there is no doubt that prevention is desirable, the evidence of the benefits of early intervention and treatment for older hospitalised patients with dementia is unclear. Aim To determine the effects of DemDel, a comprehensive delirium management programme, in inpatient acute care elders with cognitive impairment. Design and methods This paper reports the quantitative part of a mixed methods study, comparing an intervention with treatment as usual using validated outcome measures. After training, ward nurses and physicians administered the intervention based on the DemDel algorithm that focused on delirium prevention, including an intensive systematic screening schedule for cognitive impairment and delirium, as well as comprehensive delirium management. The delirium management regimen included timely administration of pro re nata medication. Settings The study was conducted within four medical wards of an acute care university hospital in urban Switzerland. Participants A total of 268 patients with cognitive impairment participated in the pre/post comparison study. The intervention and treatment as usual groups consisted of 138 and 130 patients, respectively. Results Eighty-seven (32.5%) out of 268 patients developed delirium, of whom 51 (58.6%) were of mixed, 10 (11.5%) hyperactive and 26 (29.9%) hypoactive delirium subtypes. Delirium appeared within the first five days after admission in 81.6% of cases. The 44 (31.9%) patients with delirium in the intervention group with systematic delirium management had less severe episodes of delirium and required medication for management than the 43 (33.1%) delirious patients in the control group. Intervention compliance was good on three of the four units. Conclusions The DemDel programme was effective with regard to improvement of outcomes associated with delirium in patients with cognitive impairment. The intervention was feasible and possible to be embedded within routine practice on four busy general medical wards. [ABSTRACT FROM AUTHOR]

We present a feasibility study for an object-based method to characterise thunderstorm properties in simulation data from convection-permitting weather models. An existing thunderstorm tracker, the Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) algorithm, was applied to thunderstorms simulated by the Advanced Research Weather Research and Forecasting (AR-WRF) weather model at convection-permitting resolution for a domain centred on Switzerland. Three WRF microphysics parameterisations were tested. The results are compared to independent radar-based observations of thunderstorms derived using the MeteoSwiss Thunderstorms Radar Tracking (TRT) algorithm. TRT was specifically designed to track thunderstorms over the complex Alpine topography of Switzerland. The object-based approach produces statistics on the simulated thunderstorms that can be compared to object-based observation data. The results indicate that the simulations underestimated the occurrence of severe and very large hail compared to the observations. Other properties, including the number of storm cells per day, geographical storm hotspots, thunderstorm diurnal cycles, and storm movement directions and velocities, provide a reasonable match to the observations, which shows the feasibility of the technique for characterisation of simulated thunderstorms over complex terrain. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) can improve the analysis of complex and interrelated factors that place adherent people at risk of viral rebound. Our aim was to build ML model to predict RNA viral rebound from medication adherence and clinical data. Patients were followed up at the Swiss interprofessional medication adherence program (IMAP). Sociodemographic and clinical variables were retrieved from the Swiss HIV Cohort Study (SHCS). Daily electronic medication adherence between 2008–2016 were analyzed retrospectively. Predictor variables included: RNA viral load (VL), CD4 count, duration of ART, and adherence. Random Forest, was used with 10 fold cross validation to predict the RNA class for each data observation. Classification accuracy metrics were calculated for each of the 10-fold cross validation holdout datasets. The values for each range from 0 to 1 (better accuracy). 383 HIV+ patients, 56% male, 52% white, median (Q1, Q3): age 43 (36, 50), duration of electronic monitoring of adherence 564 (200, 1333) days, CD4 count 406 (209, 533) cells/mm3, time since HIV diagnosis was 8.4 (4, 13.5) years, were included. Average model classification accuracy metrics (AUC and F1) for RNA VL were 0.6465 and 0.7772, respectively. In conclusion, combining adherence with other clinical predictors improve predictions of RNA. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the causes leading to a first revision of primary total knee arthroplasty (TKA) in a specialized knee centre and compare the results with previously published data. Prospectively collected data of a consecutive number of 195 patients after primary TKA and who underwent first revision surgery after completing the diagnostic algorithm for persistent knee pain were included. Data was prospectively collected from a specialized knee centre in which the patients presented between 2015 and 2020 and retrospectively analysed. Indications for revision surgery were categorized using all available information from patients' records. Patients were divided into early (up to two years) and late revision (more than two years). Overall mean time from index to revision surgery was 3.6 years. 49% of knee revisions occurred in the first two years, 51% after two years. 86% of the patients were referred to the knee centre from other surgeons. The most frequent reason for revision was instability, followed by patellofemoral problems, extensor mechanism insufficiency and malalignment. The most frequently performed revision was complete removal and re-implantation of a semi constrained implant design (52.5%) followed by revision using a full constrained implant design (16%). Secondary patella-resurfacing as part of complete revision was carried out in 71.5% of the cases. The majority of the patients showed concurrent reasons for TKA failure with significant correlations amongst another. Furthermore, correlations were identified between indications for revision surgery and revision implant designs. In a specialized knee centre the most common indications for the first TKA revision were instability and patellofemoral and/or extensor mechanism insufficiency followed by malalignment. In most patients there was not only one failure mode, but a combination of many. It is important to establish a standardized diagnostic algorithm to facilitate comprehensive and efficient diagnostics and the optimal treatment. [ABSTRACT FROM AUTHOR]

Missing rainfall data are a major limitation for distributed hydrological modeling and climate studies. Practitioners need reliable approaches that can be employed on a daily basis, often with too limited data in space to feed complex predictive models. In this study we compare different automatic approaches for missing data imputation, including geostatistical interpolation and pattern-based estimation algorithms. We introduce two pattern-based approaches based on the analysis of historical data patterns: (i) an iterative version of K-nearest neighbor (IKNN) and (ii) a new algorithm called vector sampling (VS) that combines concepts of multiple-point statistics and resampling. Both algorithms can draw estimations from variably incomplete data patterns, allowing the target dataset to be at the same time the training dataset. Tested on five case studies from Denmark, Australia, and Switzerland, the algorithms show a different performance that seems to be related to the terrain type: on flat terrains with spatially homogeneous rain events, geostatistical interpolation tends to minimize the average error, while in mountainous regions with nonstationary rainfall statistics, data mining can recover better the rainfall patterns. The VS algorithm, requiringminimal parameterization, turns out to be a convenient option for routine application on complex and poorly gauged terrains. [ABSTRACT FROM AUTHOR]

A tailored transfusion algorithm based on viscoelastic testing in the perioperative period or in trauma patients is recommended by guidelines for bleeding management. Bleeding management strategies in neonates and children are mostly extrapolated from the adult experience, as published evidence in the youngest age group is scarce. This manuscript is intended to give a structured overview of what has been published on the use of viscoelastic testing to guide bleeding management in neonates and children. Several devices that use either the traditional viscoelastic method or resonance viscoelastography technology are on the market. Reference ranges for children have been evaluated in only some of them. As most of the hemostasis maturation processes can be observed during the first year of life, adult reference ranges for viscoelastic testing could be applied over the age of 1 year. The majority of the published trials in children are based on retrospective analyses describing the correlation between viscoelastic testing and standard laboratory testing or focusing on the prediction of bleeding. Clinically more relevant studies in pediatric patients undergoing cardiac surgery have demonstrated that the implementation of a transfusion algorithm based on viscoelastic testing has significantly reduced transfusion requirements and that this approach has enabled a rapid detection of coagulation disorders in the presence of excessive bleeding. Although further studies are urgently needed, experts have reviewed the use of a transfusion algorithm based on viscoelastic testing in children as a feasible approach, as it has been shown to improve bleeding management and rationalize blood product transfusion. [ABSTRACT FROM AUTHOR]

Objective: To develop an automated, reproducible method for delineating hospital service areas (HSAs).Data Sources/setting: Discharge data from all Swiss acute care hospitals for the years 2013 to 2016.Study Design: We derived HSAs and hospital referral regions for Switzerland using a newly developed flow-based, automated, objective, and reproducible method using all discharge data. We compared our method to the classical, partially subjective approach used to delineate the Swiss Health Care Atlas by delineating four sets of intervention-specific HSAs.Principal Findings: Based on 4 105 885 discharges, the fully automated method delineated 63 HSAs. Comparison with existing HSAs reveals good overlap and comparable measures of health utilization between the methods and shows that in the Swiss setting, our method outperforms a cluster-based approach to defining HSAs. While the classical method potentially takes an entire day to delineate the regions, our method took approximately 10 minutes.Conclusions: Hospital service areas are used to analyze differences in use of health care that may indicate underuse and overuse. Our new, fully automated, objective, and reproducible method provides a useful tool for hospital services researchers that will enable them to delineate and update patient-flow-based HSAs. [ABSTRACT FROM AUTHOR]

The urban transit routing problem is NP-Hard, referring to the design of effective bus routes on the existing road networks. Current studies mainly focus on the models and the application of algorithms, and the improvements in the operation process in the algorithm such as the construction of initial solutions and the transformation methods are not investigated in detail. In order to optimize bus routes, the initial bus route set generation method, the local search, and the global search of the flower pollination algorithm were improved. Taking the average travel time of passengers and the proportion of the number of transfers as the optimization objective, an improved initial population generative method and an improved framework of flower pollination algorithm were applied to obtain a better set of bus routes. Finally, the effectiveness of the improved algorithm was verified based on some experimental results and compared to the previous bus networks such as Mandl's Switzerland network. [ABSTRACT FROM AUTHOR]

Although the level of digitalization and automation steadily increases in radiology, billing coding for magnetic resonance imaging (MRI) exams in the radiology department is still based on manual input from the technologist. After the exam completion, the technologist enters the corresponding exam codes that are associated with billing codes in the radiology information system. Moreover, additional billing codes are added or removed, depending on the performed procedure. This workflow is time-consuming and we showed that billing codes reported by the technologists contain errors. The coding workflow can benefit from an automated system, and thus a prediction model for automated assignment of billing codes for MRI exams based on MRI log data is developed in this work. To the best of our knowledge, it is the first attempt to focus on the prediction of billing codes from modality log data. MRI log data provide a variety of information, including the set of executed MR sequences, MR scanner table movements, and given a contrast medium. MR sequence names are standardized using a heuristic approach and incorporated into the features for the prediction. The prediction model is trained on 9754 MRI exams and tested on 1 month of log data (423 MRI exams) from two MRI scanners of the radiology site for the Swiss medical tariffication system Tarmed. The developed model, an ensemble of classifier chains with multilayer perceptron as a base classifier, predicts medical billing codes for MRI exams with a micro-averaged F1-score of 97.8% (recall 98.1%, precision 97.5%). Manual coding reaches a micro-averaged F1-score of 98.1% (recall 97.4%, precision 98.8%). Thus, the performance of automated coding is close to human performance. Integrated into the clinical environment, this work has the potential to free the technologist from a non-value adding an administrative task, therefore enhance the MRI workflow, and prevent coding errors. [ABSTRACT FROM AUTHOR]

We study models for recurrent events with special emphasis on the situation where a terminal event acts as a competing risk for the recurrent events process and where there may be gaps between periods during which subjects are at risk for the recurrent event. We focus on marginal analysis of the expected number of events and show that an Aalen-Johansen type estimator proposed by Cook and Lawless is applicable in this situation. A motivating example deals with psychiatric hospital admissions where we supplement with analyses of the marginal distribution of time to the competing event and the marginal distribution of the time spent in hospital. Pseudo-observations are used for the latter purpose. [ABSTRACT FROM AUTHOR]

Objective: To validate the National Institute for Health and Care Excellence (NICE) recommended algorithms for high-sensitivity troponin (hsTn) assays in adults presenting with chest pain.Methods: International post hoc analysis of three prospective, observational studies from tertiary hospital emergency departments. The primary endpoint was cardiac death or acute myocardial infarction (AMI) within 24 hours of presentation, and the secondary endpoint was major adverse cardiac events (MACE) at 30 days.Results: 15% of patients were diagnosed with non-ST elevation myocardial infarction (MI) on admission. The hsTnI algorithm classified 2506/3128 (80.1%) of patients as 'ruled out' with 50 (2.0%) missed MI. 943/3128 (30.1%) of patients had a troponin I level below the limit of detection on admission with 2 (0.2%) missed MI. For the hsTnT algorithm, 1794/3374 (53.1%) of patients were 'ruled out' with 7 (0.4%) missed MI. 490/3374 (14.5%) of patients had a troponin T below the limit of blank on admission with no MI. MACE at 30 days occurred in 10.7% and 8.5% of patients 'ruled out' defined by the hsTnI and hsTnT algorithms, respectively.Conclusions: The NICE algorithms could identify patients with low probability of AMI within 2 hours; however, neither strategy performed as predicted by the NICE diagnostic guidance model. Additionally, the rate of MACE at 30 days was sufficiently high that the algorithms should only be used as one component of a more extensive model of risk stratification.Trial Registration Number: ACTRN12611001069943, NCT00470587; post-results. [ABSTRACT FROM AUTHOR]

Purpose: To examine the prevalence and determinants of handbike use in persons living with spinal cord injury in Switzerland.Method: A population-based cross-sectional survey in Switzerland.Results: The crude prevalence of handbike use among the 1549 participants was 22.6%, varying between 25.3% in men and 17.7% in women. Prevalence was higher in complete than in incomplete spinal cord injury (SCI) (41.5% versus 11.9% in paraplegia, 25.6% versus 11.1% in tetraplegia). Multivariable analysis of handbike use confirmed differences with lesion characteristics and gender and showed a decline with age, lowest rates in the low-income group, variation with language, but no association with level of education or cause of spinal cord injury. In total, 45.8% of users reported to engage in handcycling at least once a week. Frequent contextual reasons for refraining from handcycling were: no interest (26%); inability due to disability (20%); unfamiliarity with the handbike (19%) and financial constraints (14%).Conclusions: Conditional on the major determinants that include demographic factors and lesion characteristics, main barriers involve contextual factors that can principally be overcome. Our findings thus suggest scope for promoting handcycling as a means towards a healthy and more physically active lifestyle in persons living with SCI.Implications for RehabilitationHandcycling is an effective means of improving health and quality of life of persons with a spinal cord injury.Persons with the following traits are most likely to use the handbike: persons younger than 62 years, with a complete paraplegia, who are German-speaking (vs. French/Italian) and having a middle or high net income.Indicated reasons for not using a handbike varied by SCI characteristics and included disinterest, inability related to the level of impairment, unfamiliarity and financial costs.Barriers that involve the above mentioned contextual factors can principally be overcome by targeted policy or information campaigns. [ABSTRACT FROM PUBLISHER]

Aims and objectives This study investigated whether an intensive care unit ( ICU) stay is associated with persistent pain, anxiety and agitation in critically ill older patients. Background Patients hospitalised in the ICU are at risk for experiencing pain, anxiety and agitation, but long-term consequences for older patients have rarely been investigated. Design Prospective nonrandomised longitudinal study. Methods Pain, anxiety and agitation, measured with a numeric rating scale (0-10), were assessed in older patients (≥65 years) hospitalised in the medical-surgical ICU of a university hospital. Agitation during the ICU was assessed with the Richmond Agitation-Sedation Scale. Data collection occurred during the ICU, one week after the stay and six and 12 months after hospital discharge. Data were collected from an age-matched community-based comparison group at recruitment and after six and 12 months. Study recruitment took place from December 2008-April 2011. Results This study included 145 older patients ( ICU group) and 146 comparison group participants. Pain was higher in the ICU group one week after discharge, although pain levels in general were low. Both groups reported no or low levels of pain after six and 12 months. Anxiety levels in general were low, although higher in the ICU group one week after ICU discharge. After six and 12 months, anxiety in both groups was comparable. Throughout the study, levels of agitation were similar in both groups. Conclusions Critically ill older patients did not experience increased pain, anxiety or agitation 12 months after an ICU stay. Relevance to clinical practice This study positively shows that an ICU stay is not associated with persistent pain, anxiety and agitation thus providing additional information to older patients and their families when making intensive care treatment decisions. Adequate management of pain during and after an ICU stay may minimise the suffering of older patients. [ABSTRACT FROM AUTHOR]

Background: Most terminally ill cancer patients prefer to die at home, but a majority die in institutional settings. Research questions about this discrepancy have not been fully answered. This study applies artificial intelligence and machine learning techniques to explore the complex network of factors and the cause-effect relationships affecting the place of death, with the ultimate aim of developing policies favouring home-based end-of-life care. Methods: A data mining algorithm and a causal probabilistic model for data analysis were developed with information derived from expert knowledge that was merged with data from 116 deceased cancer patients in southern Switzerland. This data set was obtained via a retrospective clinical chart review. Results: Dependencies of disease and treatment-related decisions demonstrate an influence on the place of death of 13%. Anticancer treatment in advanced disease prevents or delays communication about the end of life between oncologists, patients and families. Unknown preferences for the place of death represent a great barrier to a home death. A further barrier is the limited availability of family caregivers for terminal home care. The family's preference for the last place of care has a high impact on the place of death of 51%, while the influence of the patient's preference is low, at 14%. Approximately one-third of family systems can be empowered by health care professionals to provide home care through open end-of-life communication and good symptom management. Such intervention has an influence on the place of death of 17%. If families express a convincing preference for home care, the involvement of a specialist palliative home care service can increase the probability of home deaths by 24%. Conclusion: Concerning death at home, open communication about death and dying is essential. Furthermore, for the patient preference for home care to be respected, the family's decision for the last place of care seems to be key. The early initiation of family-centred palliative care and the provision of specialist palliative home care for patients who wish to die at home are suggested. [ABSTRACT FROM AUTHOR]