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In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design, is based on the explainable k-nearest neighbor algorithm and recommends a set of courses that have been passed by the majority of successful neighbors, that is, students who graduated from the study program. In terms of the number of recommended courses, we found a discrepancy between the number of courses that struggling students are recommended to take and the actual number of courses they take. This indicates that there may be an alternative path that these students could consider. However, the recommended courses align well with the courses taken by students who successfully graduated. This suggests that even students who are performing well could still benefit from the course recommender system designed for at-risk students. In the present work, we investigate a second type of success--a specific minimum number of courses passed--and compare the results with our first approach from previous work. With the second type, the information about success might be already available after one semester instead of after graduation which allows faster growth of the database and faster response to curricular changes. The evaluation of three different study programs in terms of dropout risk reduction and recommendation quality suggests that course recommendations based on students passing at least three courses in the following semester can be an alternative to guide students on a successful path.

Formative assessment is about providing and using feedback and diagnostic information. On this basis, further learning or further teaching should be adaptive and, in the best case, optimized. However, this aspect is difficult to implement in reality, as teachers work with a large number of students and the whole process of formative assessment, especially the evaluation of student performance takes a lot of time. To address this problem, this paper presents an approach in which student performance is collected through a concept map and quickly evaluated using Machine Learning techniques. For this purpose, a concept map on the topic of mechanics was developed and used in 14 physics classes in Germany. After the student maps were analysed by two human raters on the basis of a four-level feedback scheme, a supervised Machine Learning algorithm was trained on the data. The results show a very good agreement between the human and Machine Learning evaluation. Based on these results, an embedding in everyday school life is conceivable, especially as support for teachers. In this way, the teacher can use and interpret the automatic evaluation and use it in the classroom.

Through co-occurrence analysis of 1139 documents (1964-2018) we identified discussions about the implementation of student teaching evaluation (SET). We found that: (1) Attention to SET originated in the US in the 1970s, spreading to German-speaking countries in the mid-1990s and continuing in China and Latin America in the early 2000s. (2) SET is commonly viewed as a control tool deserving methodological improvement, while bias is debated in the US. We also found local trajectories: (3) Whereas in the US and Latin America SET is primarily seen as a management tool, German-speaking and Chinese authors reflect more on improving teaching. Chinese scholars consider SET a valid instrument for state control associated with artificial intelligence. Also, (4) SET is commonly used in medical education in the US and the German-speaking region and in physical education in China. We conclude that SET is discussed cross-nationally but affected by regional path dependencies.

Describes the PADOK-II project in Germany, which was designed to give information on the effects of linguistic algorithms on retrieval in a full-text database, the German Patent Information System (GPI). Relevance assessments are discussed, statistical evaluations are described, and searches are compared for the full-text section versus the abstract section. (six references) (LRW)

Structural learning theory and the Rasch model measured learning gain, retention, and transfer in 1,037 students, grades 7-10. Students learned nine functional relationships with either spontaneous or synthetic algorithms. The Rasch model gave the better description of the data. The hypothesis that the synthetic method was superior was refuted. (CP)

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Purpose: In many countries, outpatient and inpatient care are separated. During hospitalization, therefore, switching the outpatient medication to medication of the hospital formulary is required. Methods: We newly designed a switching algorithm in six switching steps (S0–S5) and conducted a study at Bundeswehr Hospital Hamburg (300 beds, 80% civilians). We performed (i) a medication reconciliation to obtain information on outpatient medications and (ii) a medication review to solve drug-related-problems, e.g., drug-drug interactions. We applied (iii) the algorithm to switch medications to the hospital formulary. Results: (i) We identified 475 outpatient medications (median per patient: 4; Q25/Q75 2/7) in 100 patients consecutively admitted to hospital (median age: 71; Q25/Q75: 64/80 years). Of 475 medications, the switching algorithm could not be used since product names were missing in 23.9% and strength in 1.7%. In 3.2%, switching was not required since medication was not prescribed during the hospital stay. (ii) Drug-drug interactions were identified in 31 of 79 patients with more than one medication. (iii) Of 475 medications, 18.5% were on the hospital formulary and therefore did not need to be switched (S0), 0.2% were on a substitution-exclusion list not allowing switching (S1), 42.0% were switched to a generic medication of the hospital formulary (S2), 1.7% to a therapeutically equivalent medication (S3), 0.4% were patient-individually switched (S4), and for 8.2% a standardized/patient-individual switching was not possible (S5). Conclusions: Despite comprehensive medication reconciliation, patient- and medication-related information for switching medications to the hospital formulary was often missing. Once all the necessary information was available, standardized switching could be easily carried out according to a newly developed switching algorithm. [ABSTRACT FROM AUTHOR]

For the German working-age population no publicly available and detailed anthropometric raw dataset exists, although several studies have collected anthropometric datasets. Unfortunately, the publication of raw data may be restricted by data usage regulations. This study presents a synthesis and validation algorithm to create a virtual copy of an already existing dataset. A detailed anthropometric dataset from a regional epidemiological public-health study in Germany was used for the synthesis and validation algorithm. Results revealed only minor deviations within the validation process. Compared to the original dataset, the virtual dataset was statistically almost identical. In a next step, the virtual dataset was weighted to approximate nationally representative values. In summary, the computed unweighted and weighted virtual data can be published without restrictions and used for ergonomic designing. Furthermore, the synthesis and validation algorithm is suitable for the generation of virtual copies and can be applied to other detailed anthropometric datasets. Data usage regulations may restrict the publication of anthropometric datasets. A synthesis and validation algorithm was developed which can be applied to existing anthropometric datasets to create a virtual copy that is almost identical and can be published. In the current study this algorithm was used for data from Germany. [ABSTRACT FROM AUTHOR]

Copyright of Anaesthesiologie & Intensivmedizin is the property of DGAI e.V. - Deutsche Gesellschaft fur Anasthesiologie und Intensivmedizin e.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tremor is one of the most common neurological symptoms. Its clinical and neurobiological complexity necessitates novel approaches for granular phenotyping. Instrumented neurophysiological analyses have proven useful, but are highly resource-intensive and lack broad accessibility. In contrast, bedside scores are simple to administer, but lack the granularity to capture subtle but relevant tremor features. We utilise the open-source computer vision pose tracking algorithm Mediapipe to track hands in clinical video recordings and use the resulting time series to compute canonical tremor features. This approach is compared to marker-based 3D motion capture, wrist-worn accelerometry, clinical scoring and a second, specifically trained tremor-specific algorithm in two independent clinical cohorts. These cohorts consisted of 66 patients diagnosed with essential tremor, assessed in different task conditions and states of deep brain stimulation therapy. We find that Mediapipe-derived tremor metrics exhibit high convergent clinical validity to scores (Spearman's ρ = 0.55–0.86, p≤.01) as well as an accuracy of up to 2.60 mm (95% CI [−3.13, 8.23]) and ≤0.21 Hz (95% CI [−0.05, 0.46]) for tremor amplitude and frequency measurements, matching gold-standard equipment. Mediapipe, but not the disease-specific algorithm, was capable of analysing videos involving complex configurational changes of the hands. Moreover, it enabled the extraction of tremor features with diagnostic and prognostic relevance, a dimension which conventional tremor scores were unable to provide. Collectively, this demonstrates that current computer vision algorithms can be transformed into an accurate and highly accessible tool for video-based tremor analysis, yielding comparable results to gold standard tremor recordings. [ABSTRACT FROM AUTHOR]

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Background: In cardiology, cardiac output (CO) is an important parameter for assessing cardiac function. While invasive thermodilution procedures are the gold standard for CO assessment, transthoracic Doppler echocardiography (TTE) has become the established method for routine CO assessment in daily clinical practice. However, a demand persists for non-invasive approaches, including oscillometric pulse wave analysis (PWA), to enhance the accuracy of CO estimation, reduce complications associated with invasive procedures, and facilitate its application in non-intensive care settings. Here, we aimed to compare the TTE and oscillometric PWA algorithm Antares for a non-invasive estimation of CO. Methods: Non-invasive CO data obtained by two-dimensional TTE were compared with those from an oscillometric blood pressure device (custo med GmbH, Ottobrunn, Germany) using the integrated algorithm Antares (Redwave Medical GmbH, Jena, Germany). In total, 59 patients undergoing elective cardiac catheterization for clinical reasons (71±10 years old, 76% males) were included. Agreement between both CO measures were assessed by Bland-Altman analysis, Student's t-test, and Pearson correlations. Results: The mean difference in CO was 0.04 ± 1.03 l/min (95% confidence interval for the mean difference: -0.23 to 0.30 l/min) for the overall group, with lower and upper limits of agreement at -1.98 and 2.05 l/min, respectively. There was no statistically significant difference in means between both CO measures (P = 0.785). Statistically significant correlations between TTE and Antares CO were observed in the entire cohort (r = 0.705, P<0.001) as well as in female (r = 0.802, P<0.001) and male patients (r = 0.669, P<0.001). Conclusions: The oscillometric PWA algorithm Antares and established TTE for a non-invasive estimation of CO are highly correlated in male and female patients, with no statistically significant difference between both approaches. Future validation studies of the Antares CO are necessary before a clinical application can be considered. [ABSTRACT FROM AUTHOR]

Background: Primary care physicians often lack resources and training to correctly diagnose and manage chronic insomnia disorder. Tools supporting chronic insomnia diagnosis and management could fill this critical gap. A survey was conducted to understand insomnia disorder diagnosis and treatment practices among primary care physicians, and to evaluate a diagnosis and treatment algorithm on its use, to identify ways to optimize it specifically for these providers. Methods: A panel of experts developed an algorithm for diagnosing and treating chronic insomnia disorder, based on current guidelines and experience in clinical practice. An online survey was conducted with primary care physicians from France, Germany, Italy, Spain, and the United Kingdom, who treat chronic insomnia patients, between January and February 2023. A sub-sample of participants provided open-ended feedback on the algorithm and gave suggestions for improvements. Results: Overall, 106 primary care physicians completed the survey. Half (52%, 55/106) reported they did not regularly screen for insomnia and half (51%, 54/106) felt they did not have enough time to address patients' needs in relation to insomnia or trouble sleeping. The majority (87%,92/106) agreed the algorithm would help diagnose chronic insomnia patients and 82% (87/106) agreed the algorithm would help improve their clinical practice in relation to managing chronic insomnia. Suggestions for improvements were making the algorithm easier to read and use. Conclusion: The algorithm developed for, and tested by, primary care physicians to diagnose and treat chronic insomnia disorder may offer significant benefits to providers and their patients through ensuring standardization of insomnia diagnosis and management. [ABSTRACT FROM AUTHOR]

Copyright of Anaesthesiologie & Intensivmedizin is the property of DGAI e.V. - Deutsche Gesellschaft fur Anasthesiologie und Intensivmedizin e.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Staffing ratios in nursing homes vary among the federal states of Germany, but there are no rational grounds for these variations. In a previous study, a new instrument for the standardized calculation of staffing requirements in nursing homes was developed (Algorithm1.0). The development was based on a new empirical data collection method that derives actual and target values for the time and number of care interventions provided. Algorithm1.0 found an increased requirement of 36% of staff in German nursing homes. Based on these results, the German legislature has commissioned a model program to trial and evaluate a complex intervention comprising increased staffing combined with strategies for organizational development. Methods: The mixed-methods study consists of (i) developing a concept for restructuring the work organization, (ii) the application of this concept combined with increased staffing in 10 nursing homes (complex intervention), and the further development of the concept using a participatory and iterative formal evaluation process. The intervention consists of (a) quantitative measures of increased staffing based on a calculation using Algorithm1.0 and (b) qualitative measures regarding organizational development. The intervention will be conducted over one year. The effects of the intervention on job satisfaction and quality of care will be evaluated in (iii) a comprehensive prospective, controlled summative evaluation. The results will be compared with ten matched nursing homes as a control group. Finally, (iv) prototypical concepts for qualification-oriented work organization, a strategy for the national rollout, and the further development of Algorithm1.0 into Algorithm 2.0 will be derived. Discussion: In Germany, there is an ongoing dynamic legislation process regarding further developing the long-term care sector. The study, which is the subject of the study protocol presented here, generates an evidence-based strategy for the staffing requirements for nursing homes. Ethics and dissemination. This study was approved by the Ethics Committee of the German Association of Nursing Science (Deutsche Gesellschaft für Pflegewissenschaft) on 02.08.2023 (amended on 20.09.2023). Research findings are disseminated through presentations at national and international conferences and publications in peer-reviewed scientific journals. Trial registration number: German Clinical Trails Register DRKS00031773 (Date of registration 09.11.2023). [ABSTRACT FROM AUTHOR]

Recent research has shown an indirect effect of generativity on fear of death through ego-integrity in older adults. The present paper aims at demonstrating that the indirect effect is valid even when controlling for social desirability. For that purpose, participants (N = 260 German adults) in study 1 provided self-reports on generativity, ego-integrity, fear of death, and social desirability. Analyses confirmed the indirect effect when the tendency for socially desirable responding was statistically controlled. In study 2, participants (N = 133 German adults) also reported on their generativity and ego-integrity. Fear of death, however, was assessed with a reaction time-based measure (i.e., the Implicit Associations Test). Again, the indirect effect could be confirmed. Taken together, the studies lend further credibility to the extant findings on the indirect effect of generativity on fear of death through ego-integrity. [ABSTRACT FROM AUTHOR]

Context: YOLO (You Look Only Once) is an algorithm based on deep neural networks with real-time object detection capabilities. This state-of-the-art technology is widely available, mainly due to its speed and precision. Since its conception, YOLO has been applied to detect and recognize traffic signs, pedestrians, traffic lights, vehicles, and so on. Objective: The goal of this research is to systematically analyze the YOLO object detection algorithm, applied to traffic sign detection and recognition systems, from five relevant aspects of this technology: applications, datasets, metrics, hardware, and challenges. Method: This study performs a systematic literature review (SLR) of studies on traffic sign detection and recognition using YOLO published in the years 2016–2022. Results: The search found 115 primary studies relevant to the goal of this research. After analyzing these investigations, the following relevant results were obtained. The most common applications of YOLO in this field are vehicular security and intelligent and autonomous vehicles. The majority of the sign datasets used to train, test, and validate YOLO-based systems are publicly available, with an emphasis on datasets from Germany and China. It has also been discovered that most works present sophisticated detection, classification, and processing speed metrics for traffic sign detection and recognition systems by using the different versions of YOLO. In addition, the most popular desktop data processing hardwares are Nvidia RTX 2080 and Titan Tesla V100 and, in the case of embedded or mobile GPU platforms, Jetson Xavier NX. Finally, seven relevant challenges that these systems face when operating in real road conditions have been identified. With this in mind, research has been reclassified to address these challenges in each case. Conclusions: This SLR is the most relevant and current work in the field of technology development applied to the detection and recognition of traffic signs using YOLO. In addition, insights are provided about future work that could be conducted to improve the field. [ABSTRACT FROM AUTHOR]

Porphyrias, as most rare diseases, are characterized by complexity and scarcity of knowledge. A national registry in one of the largest European populations that prospectively collects longitudinal clinical and laboratory data are an important and effective tool to close this gap. The German Porphyria Registry (PoReGer) was founded by four centers with longstanding expertise in the field of porphyrias and rare diseases (Charité–Universitätsmedizin Berlin, Porphyria Center Saxony Chemnitz, University Medical Center Hamburg-Eppendorf, University Medical Center Göttingen) and the German reference laboratory for porphyria, and is supported by the largest German porphyria patient organization. A specified data matrix for three subgroups (acute, chronic blistering cutaneous, acute non-blistering cutaneous) includes data on demographics, specific porphyria-related symptoms, clinical course, general medical history, necessary follow-up assessments (including laboratory and imaging results), symptomatic and disease-modifying therapies, and side-effects. Additionally, the registry includes patient-reported outcome measures on quality of life, depression, and fatigue. PoReGer aims to broaden and deepen the understanding on all porphyria-related subjects. We expect these data to significantly improve the management and care of porphyria patients. Additionally, the data can be used for educational purposes to increase awareness, for the planning of healthcare services, and for machine learning algorithms for early detection of porphyrias. [ABSTRACT FROM AUTHOR]

Detecting changes in soil micro-relief in farmland helps to understand degradation processes like sheet erosion. Using the high-resolution technique of terrestrial laser scanning (TLS), we generated point clouds of three 2 × 3 m plots on a weekly basis from May to mid-June in 2022 on cultivated farmland in Germany. Three well-known applications for eliminating vegetation points in the generated point cloud were tested: Cloth Simulation Filter (CSF) as a filtering method, three variants of CANUPO as a machine learning method, and ArcGIS PointCNN as a deep learning method, a sub-category of machine learning using deep neural networks. We assessed the methods with hard criteria such as F1 score, balanced accuracy, height differences, and their standard deviations to the reference surface, resulting in data gaps and robustness, and with soft criteria such as time-saving capacity, accessibility, and user knowledge. All algorithms showed a low performance at the initial measurement epoch, increasing with later epochs. While most of the results demonstrate a better performance of ArcGIS PointCNN, this algorithm revealed an exceptionally low performance in plot 1, which is describable by the generalization gap. Although CANUPO variants created the highest amount of data gaps, we recommend that CANUPO include colour values in combination with CSF. [ABSTRACT FROM AUTHOR]

Copyright of Die Urologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Telemedicine has been shown to improve the outcome of heart failure (HF) patients in addition to medical and device therapy. We investigate the effectiveness of a comprehensive telehealth programme in patients with recent hospitalisation for HF on subsequent HF hospitalisations and mortality compared to usual care in a real-world setting. The telehealth programme consists of daily remote telemonitoring of HF signs/symptoms and regular individualised telecoaching sessions. Between January 2018 and September 2020, 119,715 patients of a German health insurer were hospitalised for HF and were eligible for participation in the programme. Finally, 6065 HF patients at high risk for re-hospitalisation were enroled. Participants were retrospectively compared to a propensity score matched usual care group (n = 6065). Median follow-up was 442 days (IQR 309–681). Data from the health insurer was used to evaluate outcomes. After one year, the number of hospitalisations for HF (17.9 vs. 21.8 per 100 patient years, p < 0.001), all-cause hospitalisations (129.0 vs. 133.2 per 100 patient years, p = 0.015), and the respective days spent in hospital (2.0 vs. 2.6 days per year, p < 0.001, and 12.0 vs. 13.4, p < 0.001, respectively) were significantly lower in the telehealth than in the usual care group. Moreover, participation in the telehealth programme was related to a significant reduction in all-cause mortality compared to usual care (5.8 vs. 11.0 %, p < 0.001). In a real-life setting of ambulatory HF patients at high risk for re-hospitalisation, participation in a comprehensive telehealth programme was related to a reduction of HF hospitalisations and all-cause mortality compared to usual care. [ABSTRACT FROM AUTHOR]

In March 1964, the cover page of a popular German weekly magazine entitled Der Spiegel painted a frightening picture: An anthropomorphic robot with six mechanical arms commands an assembly line while a displaced human worker floats aimlessly in the foreground. Ejected from his station, the worker throws up his hands in despair next to a headline that reads, "Automation in Germany, the arrival of robots." Over fifty years later, a cover page from the same magazine evoked similar themes: A giant robot arm yanks an office worker away from his computer under the headline, "You're fired! How computers and robots steal our jobs - and which jobs will be safe." The more things change, the more they stay the same. [ABSTRACT FROM AUTHOR]

Recently an individualisation algorithm has been developed and shown to significantly improve the diagnostic accuracy of creatine kinase (CK) and urea in endurance sports and Badminton. In this study, the applicability and benefit of this algorithm was evaluated using repeated measures data from 161 professional German soccer players monitored during the 2015–2017 seasons. Venous blood samples were collected after a day off (recovered state) and after a minimum of two strenuous training sessions within 48 h (non-recovered state) and analysed for CK and urea. Group-based reference ranges were derived from that same dataset to ensure the best possible reference for comparison. A z-test was conducted to analyse differences in error rates between individualised and group-based classifications. CK values for the individualised approach showed significantly lower error rates in the assessment of muscle recovery compared to both a population-based (p <.001; z-value: −17.01; test-pass error rate: 21 vs. 67%; test-fail: 19 vs. 64%) and a group-based cut-off (p <.001; z-value: −15.29; test-pass error rate: 65%; test-fail: 67%). It could be concluded that the assessment of muscle recovery in soccer using individualised interpretations of blood-borne markers may offer higher diagnostic accuracy than a population-based and a sample-specific group-based approach. Highlights Assessing muscle recovery via CK using individualised ranges seems to offer a higher diagnostic accuracy than a sample-specific group-based analysis. Using an individualised algorithm seems to be a promising approach to overcome diagnostic problems arising from large inter- and intraindividual variability in blood parameters as it significantly improved the diagnostic accuracy of CK as a recovery marker. As recovery assessment in elite soccer ultimately aims at the accurate detection of differences in the individual player this algorithm seems to offer coaches and sport scientists a more sensitive approach compared to group-specific evaluations. [ABSTRACT FROM AUTHOR]

Introduction: This pilot study aimed to investigate the effects of using an app-based certified medical product named PINK! on breast cancer patients and survivors. The objectives were to measure psychological distress, physical activity, and therapy-related fatigue of patients using PINK! to identify trends and develop a study design for a subsequent multicentric proof of efficacy RCT. Materials and Methods: PINK! offers individualized, evidence-based therapy and side-effect management, mindfulness-based stress reduction, nutritional and psychological education, physical activity tracking, and motivational exercises to implement lifestyle changes sustainably in daily routine. A prospective, intraindividual RCT was performed with n = 60 patients in 2021 at Comprehensive Cancer Center Munich. Patients with BC were included independent of the stage of diseases. The intervention group got access to PINK! over 12 weeks. Control group served as a waiting-list comparison to "standard of care." Results: Primary efficacy variable analysis revealed a relative average decrease of 32.9% in psychological distress, which corresponds to a statistically significant reduction (p < 0.001) within 12 weeks compared to the control group. Linear regressions within usage groups showed a correlation of high app usage and a reduction of psychological distress. Fatigue data presented a statistically significant antifatigue efficacy (p < 0.001) and physical activity increased by 63.9%. Conclusion: App-based supportive care offers a promising, low-threshold, and cost-efficient opportunity to improve psychological well-being, quality of life, fatigue, and physical activity. More research is needed to implement eHealth solutions in clinical cancer care. [ABSTRACT FROM AUTHOR]

Human Papilloma Virus (HPV)-associated oropharyngeal squamous cell cancer (OPSCC) represents an OPSCC subgroup with an overall good prognosis with a rising incidence in Western countries. Multiple lines of evidence suggest that HPV-associated tumors are not a homogeneous tumor entity, underlining the need for accurate prognostic biomarkers. In this retrospective, multi-institutional study involving 906 patients from four centers and one database, we developed a deep learning algorithm (OPSCCnet), to analyze standard H&E stains for the calculation of a patient-level score associated with prognosis, comparing it to combined HPV-DNA and p16-status. When comparing OPSCCnet to HPV-status, the algorithm showed a good overall performance with a mean area under the receiver operator curve (AUROC) = 0.83 (95% CI = 0.77-0.9) for the test cohort (n = 639), which could be increased to AUROC = 0.88 by filtering cases using a fixed threshold on the variance of the probability of the HPV-positive class - a potential surrogate marker of HPV-heterogeneity. OPSCCnet could be used as a screening tool, outperforming gold standard HPV testing (OPSCCnet: five-year survival rate: 96% [95% CI = 90–100%]; HPV testing: five-year survival rate: 80% [95% CI = 71–90%]). This could be confirmed using a multivariate analysis of a three-tier threshold (OPSCCnet: high HR = 0.15 [95% CI = 0.05–0.44], intermediate HR = 0.58 [95% CI = 0.34–0.98] p = 0.043, Cox proportional hazards model, n = 211; HPV testing: HR = 0.29 [95% CI = 0.15–0.54] p < 0.001, Cox proportional hazards model, n = 211). Collectively, our findings indicate that by analyzing standard gigapixel hematoxylin and eosin (H&E) histological whole-slide images, OPSCCnet demonstrated superior performance over p16/HPV-DNA testing in various clinical scenarios, particularly in accurately stratifying these patients. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the current practice regarding the prevalence and sequence of x-ray and CT scan in diagnostic algorithms for multiple injured patients. Methods: All primarily treated patients with ISS ≥ 9 were selected from the TraumaRegister DGU® (years 2008–2015; n = 109,257). Four subgroups of diagnostic algorithm were defined: CT only (group C; n = 63,763), CT before x-ray (group CX; n = 3711), x-ray followed by CT (group XC; n = 33,590), and x-ray only (group X, n = 8193). We analysed the type and sequence of diagnostic procedures and their association with hospital mortality and length-of-stay in the emergency room (ER-LOS). Results: Predominant strategies were CT only (58.4%) and x-ray followed by CT (30.7%). Overall mortality was between 10 and 12% in all subgroups involving CT, and 6.6% in the x-ray only group. Expected mortality was within the 95% confidence of observed mortality except for the CX group (observed 10.0%; CI95 8.9–11.0; expected 11.1%). Mean / median length of stay in the emergency room was shortest in the CT only subgroup: (60 / 50 min). Prior x-ray diagnostic resulted in additional 3 min (group XC). The use of additional x-ray diagnostic decreased from 51.6% (in 2008) to 35.4% (in 2015). Conclusions: ER-LOS is significantly affected by diagnostic pathway. CT scan alone accelerates ER-LOS, which however was not associated with lower mortality rates. Performing completive x-ray examinations after an initial CT scan seems not to deteriorate mortality rates. [ABSTRACT FROM AUTHOR]

Background: In the event of a mass casualty incident (MCI), the situation-related shortage of medical resources does not end when the patients are transported from the scene of the incident. Consequently, an initial triage is required in the receiving hospitals. In the first step, the aim of this study was to create a reference patient vignette set with defined triage categories. This allowed a computer-aided evaluation of the diagnostic quality of triage algorithms for MCI situations in the second step. Methods: A total of 250 case vignettes validated in practice were entered into a multistage evaluation process by initially 6 and later 36 triage experts. This algorithm—independent expert evaluation of all vignettes—served as the gold standard for analyzing the diagnostic quality of the following triage algorithms: Manchester triage system (MTS module MCI), emergency severity index (ESI), Berlin triage algorithm (BER), the prehospital algorithms PRIOR and mSTaRT, and two project algorithms from a cooperation between the Federal Office of Civil Protection and Disaster Assistance (BBK) and the Hashemite Kingdom of Jordan—intrahospital Jordanian-German project algorithm (JorD) and prehospital triage algorithm (PETRA). Each patient vignette underwent computerized triage through all specified algorithms to obtain comparative test quality outcomes. Results: Of the original 250 vignettes, a triage reference database of 210 patient vignettes was validated independently of the algorithms. These formed the gold standard for comparison of the triage algorithms analyzed. Sensitivities for intrahospital detection of patients in triage category T1 ranged from 1.0 (BER, JorD, PRIOR) to 0.57 (MCI module MTS). Specificities ranged from 0.99 (MTS and PETRA) to 0.67 (PRIOR). Considering Youden's index, BER (0.89) and JorD (0.88) had the best overall performance for detecting patients in triage category T1. Overtriage was most likely with PRIOR, and undertriage with the MCI module of MTS. Up to a decision for category T1, the algorithms require the following numbers of steps given as the median and interquartile range (IQR): ESI 1 (1–2), JorD 1 (1–4), PRIOR 3 (2–4), BER 3 (2–6), mSTaRT 3 (3–5), MTS 4 (4–5) and PETRA 6 (6–8). For the T2 and T3 categories the number of steps until a decision and the test quality of the algorithms are positively interrelated. Conclusion: In the present study, transferability of preclinical algorithm-based primary triage results to clinical algorithm-based secondary triage results was demonstrated. The highest diagnostic quality for secondary triage was provided by the Berlin triage algorithm, followed by the Jordanian-German project algorithm for hospitals, which, however, also require the most algorithm steps until a decision. [ABSTRACT FROM AUTHOR]

Background: Information on the prevalence of temporomandibular disorders (TMD) or possible/probable bruxism in seniors is heterogeneous and sparse. Objectives: To elucidate the prevalence of TMD and possible/probable bruxism in German adults aged 60 years and older. Methods: Participants of the Interdisciplinary Longitudinal Study of Adult Development and Aging (ILSE) born between 1950–1952 (C1) and 1930–1932 (C2) were examined in 2014–2016 (fourth wave). The participants were surveyed and clinically examined by one calibrated examiner. Two questions of the Patient Health Questionnaire (PHQ) were utilised to evaluate self‐reported bruxism. The clinical examination included signs of probable bruxism and the RDC/TMD examination protocol. Results: Data from 191 participants were available. No RDC/TMD diagnosis was made in 83.2%. Of the participants, 15.2% received a single diagnosis and 1.6% multiple diagnoses that included disc displacements (9.4%) and degenerative joint diseases (8.9%). A total of 24.7% reported bruxism that included self‐reported awake bruxism in 11.9% and sleep bruxism in 16.2%. Wear was clinically identified in 27.2% of the participants. No sex‐related differences were observed. Significant differences were detected for probable bruxism between C1 (14.1%) and C2 (54.3%). Conclusion: In the German population aged 60 years and older, the prevalence of TMD is 16.8%. TMD is characterised by temporomandibular joint disorders, including disc displacements and degenerative joint disorders. Bruxism was observed in a quarter of the old population. [ABSTRACT FROM AUTHOR]

Since early 2020, the world has been dealing with the COVID-19 pandemic. The rapid changing situation led to unforeseeable challenges and questions for many people, including pregnant women. Through a textual analysis of personal narratives told via pregnancy and/or laboring vlogs during COVID-19, this present study aims to understand how women from China who live in another country during pregnancy have utilized YouTube vlogs to share their experiences. Through this analysis, we identify various challenges that these women experienced during their pregnancy. The COVID-19 pandemic exaggerated the normal difficulties of these issues and also created additional problems for these women, including regular pregnancy tests, choice of birthing locations, and the support and caring that were normal during this time period. [ABSTRACT FROM AUTHOR]

Grammatical knowledge has often been investigated in wug tests, in which participants inflect pseudo-words. It was argued that in inflecting these pseudo-words, speakers apply their knowledge of word formation. However, it remains unclear what exactly this knowledge is and how it is learned. According to one theory, the knowledge is best characterised as abstractions that specify how units are combined. Another theory maintains that it is best characterised by memory-based analogy. In both cases the knowledge is learned by association based on positive evidence alone. In this paper, we model the classification of pseudo-words to Maltese plurals using a shallow neural network trained with an error-driven learning algorithm. We demonstrate that the classifications mirror those of Maltese speakers in a wug test. Our results indicate that speakers rely on gradient knowledge of a relation between the phonetics of whole words and plural classes, which is learned in an error-driven way. [ABSTRACT FROM AUTHOR]

Background: The present study evaluated the implementation of the European Resuscitation Council Corona-Virus-Disease 2019 (COVID-19) resuscitation guidelines in Germany 1 year after publication. Aim of the work: To evaluate the practical implementation of the COVID-19 resuscitation guidelines in Germany one year after their publication. Material and methods: In an online survey between April and May 2021 participants were asked about awareness of COVID-19 resuscitation guidelines, corresponding training, the resuscitation algorithm used and COVID-19 infections of emergency medicine personnel associated with COVID-19 resuscitation. Results: A total of 961 (8%) of the 11,000 members took part in the survey and 85% (818/961) of questionnaires were fully completed. While 577 (70%) of the respondents were aware of the COVID-19 guidelines, only 103 (13%) had received respective training. A specific COVID-19 resuscitation algorithm was used by 265 respondents (32%). Adaptations included personal protective equipment (99%), reduction of staff caring for the patient, or routine use of video laryngoscopy for endotracheal intubation (each 37%), securing the airway before rhythm analysis (32%), and pausing chest compressions during endotracheal intubation (30%). Respondents without a specific COVID-19 resuscitation algorithm were more likely to use mouth-nose protection (47% vs. 31%; p < 0.001), extraglottic airway devices (66% vs. 55%; p = 0.004) and have more than 4 team members close to the patient (45% vs. 38%; p = 0.04). Use of an Filtering-Face-Piece(FFP)-2 or FFP3 mask (89% vs. 77%; p < 0.001; 58% vs. 70%; p ≤ 0.001) or performing primary endotracheal intubation (17% vs. 31%; p < 0.001) were found less frequently and 9% reported that a team member was infected with COVID-19 during resuscitation. Conclusion: The COVID-19 resuscitation guidelines are still insufficiently implemented 1 year after publication. Future publication strategies must ensure that respective guideline adaptations are implemented in a timely manner. [ABSTRACT FROM AUTHOR]

Narcissism is a multifaceted construct commonly conceptualized as comprising grandiose and vulnerable aspects in a two-factor model. While the manifold correlates of these aspects imposed a challenge for research on the structure of narcissism, recent models converge in a three-factor structure of agentic-extraverted, antagonistic, and neurotic aspects, capturing variance in different conceptualizations and correlates of narcissism. We construct and validate a German adaptation of the Five-Factor Narcissism Inventory (FFNI), a measure assessing these aspects based on the Five-Factor Model. In eight samples (N = 2,921), we found the German FFNI to align with both, two- and three-factor models. The factors display good criterion validity with other narcissism measures, (non-)clinical personality dimensions, interpersonal styles, and (mal-)adaptive adjustment. Neurotic and antagonistic narcissism discriminated between individuals with/without mental disorder diagnoses, and displayed a characteristic profile in incarcerated offenders. Since the FFNI is comprehensive but long, we constructed a 30-item brief form (FFNI-BF) optimizing the internal structure and external validity using ant colony optimization. The FFNI-BF displayed good psychometric characteristics and similar, in certain aspects even advantageous criterion validity. We conclude that the German FFNI validly measures key aspects of narcissism, and the FFNI-BF captures these in a concise manner. [ABSTRACT FROM AUTHOR]

Background Postoperative delirium (POD) is a frequent complication in older adults, characterised by disturbances in attention, awareness and cognition, and associated with prolonged hospitalisation, poor functional recovery, cognitive decline, long-term dementia and increased mortality. Early identification of patients at risk of POD can considerably aid prevention. Methods We have developed a preoperative POD risk prediction algorithm using data from eight studies identified during a systematic review and providing individual-level data. Ten-fold cross-validation was used for predictor selection and internal validation of the final penalised logistic regression model. The external validation used data from university hospitals in Switzerland and Germany. Results Development included 2,250 surgical (excluding cardiac and intracranial) patients 60 years of age or older, 444 of whom developed POD. The final model included age, body mass index, American Society of Anaesthesiologists (ASA) score, history of delirium, cognitive impairment, medications, optional C-reactive protein (CRP), surgical risk and whether the operation is a laparotomy/thoracotomy. At internal validation, the algorithm had an AUC of 0.80 (95% CI: 0.77–0.82) with CRP and 0.79 (95% CI: 0.77–0.82) without CRP. The external validation consisted of 359 patients, 87 of whom developed POD. The external validation yielded an AUC of 0.74 (95% CI: 0.68–0.80). Conclusions The algorithm is named PIPRA (Pre-Interventional Preventive Risk Assessment), has European conformity (ce) certification, is available at http://pipra.ch/ and is accepted for clinical use. It can be used to optimise patient care and prioritise interventions for vulnerable patients and presents an effective way to implement POD prevention strategies in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Transfusion of red blood cell concentrate can be life-saving, but requires accurate dose calculations in children. Aims: We tested the hypothesis that cognitive aids would improve identification of the correct recommended volumes and products, according to the German National Transfusion guidelines, in pediatric transfusion scenarios. Methods: Four online questionnaire-based scenarios, two with hemodynamically stable and two with hemodynamically unstable children, were sent to German and international pediatric anesthetists for completion. In the two stable scenarios, participants were given pre-filled tables that contained all required information. For the two emergency scenarios, existing algorithms were used and required calculation by the user. The results were classified into three categories of deviations from the recommended values (DRV): DRV120 (<80% or >120%), as the acceptable variation; DRV 300 (<33% or >300%), the deviation of concern for potential harm; and DRV 1000 (<10% or >1000%), the excessive deviation with a high probability of harm. Results: A total of 1.458 pediatric anesthetists accessed this simulation questionnaire, and 402 completed questionnaires were available for analysis. A pre-filled tabular aid, avoiding calculations, led to a reduction in deviation rates in the category of DRV120 by 60% for each and of DRV300 by 17% and 20%, respectively. The use of algorithms as aids for unstable emergencies led to a reduction in the deviation rate only for DRV120 (20% and 15% respectively). In contrast, the deviation rates for DRV300 and DRV1000 rose by 37% and 16%, respectively. Participants used higher transfusion thresholds for the emergency case of a 2-year-old compromised child than for the stable case with a patient of the same age (on average, 8.6 g/dL, 95% CI 8.5–8.8 versus 7.1 g/dL, 95% CI 7.0–7.2, p < 0.001) if not supported by our aids. Participants also used a higher transfusion threshold for unstable children aged 3 months than for stable children of the same age (on average, 8.9 g/dL, 95% CI 8.7–9.0 versus 7.9 g/dL, 95% CI 7.7–8.0, p < 0.001). Conclusions: The use of cognitive aids with precalculated transfusion volumes for determining transfusion doses in children may lead to improved adherence to published recommendations, and could potentially reduce dosing deviations outside those recommended by the German national transfusion guidelines. [ABSTRACT FROM AUTHOR]

The Bat optimizing algorithm (BOA) is one of the metaheuristic algorithms and applied here to interpret self-potential (SP) data. The BOA is depending upon a bat echolocation behavior for global optimization, which the global optimum solution reached at the suggested minimum value of the objective function. The best interpretive source parameters for the subsurface structures occurred at the minimal the objective function value (global best solution). The BOA is applied to 2D SP anomaly data to estimate the characteristic source parameters (i.e., the depth to center, amplitude coefficient, origin location, geometric shape factor, and polarization and inclination angle of the causative buried structure). The BOA can be applied to single and multiple source structures in the restricted class of simple geometric shapes, which these bodies help in the validation of the subsurface ore and mineral targets. The stability and efficiency of the proposed BOA have been examined by several synthetic examples. In addition, three different real field examples from Germany and Indonesia have been successfully applied to ore and mineral investigation and geological structure studies. In general, the achieved results are in good agreement with the available borehole data and results mentioned in the literature. [ABSTRACT FROM AUTHOR]

The demand of accurate, near-real-time radar-based quantitative precipitation estimation (QPE), which is key to feed hydrological models and enable reliable flash flood predictions, was highlighted again by the disastrous floods following after an intense stratiform precipitation field passing western Germany on 14 July 2021. Three state-of-the-art rainfall algorithms based on reflectivity Z, specific differential phase KDP, and specific attenuation A were applied to observations of four polarimetric C-band radars operated by the German Meteorological Service [DWD (Deutscher Wetterdienst)]. Due to the large vertical gradients of precipitation below the melting layer suggesting warm-rain processes, all QPE products significantly underestimate surface precipitation. We propose two mitigation approaches: (i) vertical profile (VP) corrections for Z and KDP and (ii) gap filling using observations of a local X-band radar, JuXPol. We also derive rainfall retrievals from vertically pointing Micro Rain Radar (MRR) profiles, which indirectly take precipitation gradients in the lower few hundreds of meters into account. When evaluated with DWD rain gauge measurements, those retrievals result in pronounced improvements, especially for the A-based retrieval partly due to its lower sensitivity to the variability of raindrop size distributions. The VP correction further improves QPE by reducing the normalized root-mean-square error by 23% and the normalized mean bias by 20%. With the use of gap-filling JuXPol data, the A-based retrieval gives the lowest errors followed by the Z-based retrievals in combination with VP corrections. The presented algorithms demonstrate the increased value of radar-based QPE application for warm-rain events and related potential flash flooding warnings. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

The shortage of grafts for liver transplantation requires risk stratification and adequate allocation rules. This study aims to improve the model of end-stage liver disease (MELD) score for 90-day mortality prediction with the help of different machine-learning algorithms. We retrospectively analyzed the clinical and laboratory data of 654 patients who were recruited during the evaluation process for liver transplantation at University Hospital Leipzig. After comparing 13 different machine-learning algorithms in a nested cross-validation setting and selecting the best performing one, we built a new model to predict 90-day mortality in patients with end-stage liver disease. Penalized regression algorithms yielded the highest prediction performance in our machine-learning algorithm benchmark. In favor of a simpler model, we chose the least absolute shrinkage and selection operator (lasso) regression. Beside the classical MELD international normalized ratio (INR) and bilirubin, the lasso regression selected cystatin C over creatinine, as well as IL-6, total protein, and cholinesterase. The new model offers improved discrimination and calibration over MELD and MELD with sodium (MELD-Na), MELD 3.0, or the MELD-Plus7 risk score. We provide a new machine-learning-based model of end-stage liver disease that incorporates synthesis and inflammatory markers and may improve the classical MELD score for 90-day survival prediction. [ABSTRACT FROM AUTHOR]

Several scholarly studies and journalistic investigations have found that automated decision-making in welfare systems burdens claimants by forecasting their behaviour, targeting them for sanctions and surveillance and punishing them without revealing the underlying mechanisms driving such decisions. This article develops an analytical framework combining three areas of concern regarding automation: how it might introduce surveillance and social sorting, how it can entail the loss of human discretion, and how it requires new systems of governance and due process. This framework steers investigations into whether and how automated decision-making welfare systems introduce new harms and burdens for claimants. A case study on automation processes in Germany's unemployment benefit service's IT system, ALLEGRO, applies this approach and finds that this system does allow for broad human discretion and avoids some forms of surveillance, such as risk-assessments from historic data, though it nevertheless increases surveillance of claimants through sharing data with external agencies. The developed framework also suggests that concerns raised in one area -- whether loss of human discretion, surveillance, or lack of due process -- can be mitigated by attending to the other two areas and urges researchers and policy-makers to attend to the mitigating or reinforcing factors of each concern. [ABSTRACT FROM AUTHOR]

The fundamental equations for the evaluation of cylindrical involute gear measurements on 3D gear measuring instruments are provided. The computations are based on the principles of gear kinematics and use the system of involute gear coordinates introduced in a previous work of the authors. This holistic approach focuses on significant error sources that only appear since 3D measurement technology is used and that are almost unrecognized till today. The proposed algorithms are beneficial for the description of gear deviations as they allow the use of simple formulas covering profile, helix and pitch evaluation for internal or external and spur or helical gears. The presented equations contain the key fundamentals to complement existing standards. They will become part of reference algorithms used by the Physikalisch-Technische Bundesanstalt, the national metrology institute of Germany, to certify gear evaluation software. [ABSTRACT FROM AUTHOR]

Assessing the impact of dysarthria on a patient's ability to communicate should be an integral part of patient management. However, due to the high demands on reliable quantification of communication limitations, hardly any formal clinical tests with approved psychometric properties have been developed so far. This study investigates a web-based assessment of communication impairment in dysarthria, named KommPaS. The test comprises measures of intelligibility, naturalness, perceived listener effort and communication efficiency, as well as a total score that integrates these parameters. The approach is characterized by a quasi-random access to a large inventory of test materials and to a large group of naïve listeners, recruited via crowdsourcing. As part of a larger research program to establish the clinical applicability of this new approach, the present paper focuses on two psychometric issues, namely specificity and sensitivity (study 1) and retest-reliability (study 2). Study 1: KommPaS was administered to 54 healthy adults and 100 adult persons with dysarthria (PWD). Non-parametric criterion-based norms (specificity: 0.95) were used to derive a standard metric for each of the four component variables, and corresponding sensitivity values for the presence of dysarthria were identified. Overall classification accuracy of the total score was determined using a ROC analysis. The resulting cutscores showed a high accuracy in the separation of PWD from healthy speakers for the naturalness and the total score. Study 2: A sub-group of 20 PWD enrolled in study 1 were administered a second KommPaS examination. ICC analyses revealed good to excellent retest reliabilities for all parameters. [ABSTRACT FROM AUTHOR]

With its standardized MRI datasets of the entire spine, the German National Cohort (GNC) has the potential to deliver standardized biometric reference values for intervertebral discs (VD), vertebral bodies (VB) and spinal canal (SC). To handle such large-scale big data, artificial intelligence (AI) tools are needed. In this manuscript, we will present an AI software tool to analyze spine MRI and generate normative standard values. 330 representative GNC MRI datasets were randomly selected in equal distribution regarding parameters of age, sex and height. By using a 3D U-Net, an AI algorithm was trained, validated and tested. Finally, the machine learning algorithm explored the full dataset (n = 10,215). VB, VD and SC were successfully segmented and analyzed by using an AI-based algorithm. A software tool was developed to analyze spine-MRI and provide age, sex, and height-matched comparative biometric data. Using an AI algorithm, the reliable segmentation of MRI datasets of the entire spine from the GNC was possible and achieved an excellent agreement with manually segmented datasets. With the analysis of the total GNC MRI dataset with almost 30,000 subjects, it will be possible to generate real normative standard values in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: The outbreak of the coronavirus disease 2019 (COVID-19) is a major challenge for healthcare systems and impedes cancer screening programs worldwide. However, no research has been performed that analyzes the effects of the COVID-19 pandemic on the newly implemented German Cervical Cancer Screening (CCS) program. Here, we showed a severe clinically relevant decrease of patients receiving CCS in Germany between the pre-pandemic time period of 2018–2019 and the pandemic years of 2020–2021. Given that this sudden drop in number of CCS per practice is unlikely, our data suggest a dramatic number of underdiagnosed cervical precancer lesions that may lead to increased cervical cancer burden in the future. Purpose: the aim of this cross-sectional study was to analyze the impact of the COVID-19 pandemic on Cervical Cancer Screening (CCS) in gynecological practices in Germany. Methods: The basis of the analysis was the Uniform Evaluation Standard (EBM) of the Statutory Health Insurance Scheme. This cross-sectional study included all women aged ≥20 years with at least one CCS (clinical and cytological examination) in 223 gynecological practices in Germany during the period 2018–2021. The number of patients with CCS per practice was shown for each year. The average number of patients per year was compared between the pre-pandemic time period (2018, 2019) and the pandemic time period (2020, 2021) using Wilcoxon tests. Analyses were conducted separately for clinical investigations and cytological investigations and were also stratified by age group (20–34, 35–50, >50 years). Results: CCS in gynecological practices significantly decreased in Germany between the pre-pandemic time period of 2018–2019 and the pandemic years of 2020–2021. This decrease was observed in all age groups but was stronger in women aged 20–34 (−25.6%) and weaker in women aged >50 (−15.2%). Conclusions: We found a statistically and clinically relevant decrease of patients receiving CCS in gynecological practices in Germany. This finding is even more exceptional because the new screening algorithm with direct invitations for each patient started in 2020 and was supposed to lead to a higher number of patients in its first years. However, the observed decline in the detection of cervical precancer lesions may lead to increased cervical cancer burden. Risk-based screening strategies and further measures are necessary to adapt to the ongoing COVID-19 pandemic and return to pre-pandemic CCS numbers. [ABSTRACT FROM AUTHOR]

Automatic fault detection (AFD) is a key technology to optimize the Operation and Maintenance of photovoltaic (PV) systems portfolios. A very common approach to detect faults in PV systems is based on the comparison between measured and simulated performance. Although this approach has been explored by many authors, due to the lack a common basis for evaluating their performance, it is still unclear what are the influencing aspects in the design of AFD algorithms. In this study, a series of AFD algorithms have been tested under real operating conditions, using monitoring data collected over 58 months on 80 rooftop-type PV systems installed in Germany. The results shown that this type of AFD algorithm have the potential to detect up to 82.8% of the energy losses with specificity above 90%. In general, the higher the simulation accuracy, the higher the specificity. The use of less accurate simulations can increase sensitivity at the cost of decreasing specificity. Analyzing the measurements individually makes the algorithm less sensitive to the simulation accuracy. The use of machine learning clustering algorithm for the statistical analysis showed exceptional ability to prevent false alerts, even in cases where the modeling accuracy is not high. If a slightly higher level of false alerts can be tolerated, the analysis of daily PR using a Shewhart chart provides the high sensitivity with an exceptionally simple solution with no need for more complex algorithms for modeling or clustering. • Recommendations for designing automatic fault detection algorithms for PV systems. • Results based on tests with 5 years monitoring data from 80 rooftop-type PV systems. • Statistical analysis based on clustering shows very few false alerts in all cases. • Using less accurate modeling increases sensitivity, but decreases specificity. • Daily PR monitoring with control chart can be a reasonable and simple solution. [ABSTRACT FROM AUTHOR]

Background: Retained rectal foreign bodies (RFBs) are uncommon clinical findings. Although the management of RFBs is rarely reported in the literature, clinicians regularly face this issue. To date, there is no standardized management of RFBs. The aim of the present study was to evaluate our own data and subsequently develop a treatment algorithm. Methods: All consecutive patients who presented between January 2006 and December 2019 with rectally inserted RFBs at the emergency department of the Klinikum Stuttgart, Germany, were retrospectively identified. Clinicopathologic features, management, complications, and outcomes were assessed. Based on this experience, a treatment algorithm was developed. Results: A total of 69 presentations with rectally inserted RFBs were documented in 57 patients. In 23/69 cases (33.3%), the RFB was removed transanally by the emergency physician either digitally (n = 14) or with the help of a rigid rectoscope (n = 8) or a colonoscope (n = 1). In 46/69 cases (66.7%), the RFB was removed in the operation theater under general anesthesia with muscle relaxation. Among these, 11/46 patients (23.9%) underwent abdominal surgery, either for manual extraction of the RFB (n = 9) or to exclude a bowel perforation (n = 2). Surgical complications occurred in 3/11 patients. One patient with rectal perforation developed pelvic sepsis and underwent abdominoperineal extirpation in the further clinical course. Conclusion: The management of RFBs can be challenging and includes a wide range of options from removal without further intervention to abdominoperineal extirpation in cases of pelvic sepsis. Whenever possible, RFBs should obligatorily be managed in specialized colorectal centers following a clear treatment algorithm. [ABSTRACT FROM AUTHOR]

Inpatient violence poses a great risk to the health and well-being of other patients and members of staff. Previous research has shown that prevalence rates of violent behavior are particularly high in forensic psychiatric settings. Thus, the reliable identification of forensic inpatients who are particularly at risk for violent behavior is an important aspect of risk management. In the present study, we analyzed clinicians' assessments of N = 504 male and female inpatients of German forensic mental health institutions in order to identify risk factors for verbal institutional violence. Using a tree-based modeling approach, we found the following variables to be predictors of verbal aggression: gender, insight into the illness, number of prior admissions to psychiatric hospitals, and insight into the iniquity of the offence. A high number of prior admissions to psychiatric hospitals seems to be a risk factor for verbal aggression amongst men whereas it showed the opposite effect amongst women. Our results highlight the importance of dynamic risk factors, such as poor insight into the own illness, in the prediction of violent incidents. With regard to future research, we argue for a stronger emphasis on nonparametric models as well as on potential interaction effects of risk and protective factors. [ABSTRACT FROM AUTHOR]

Working time regimes in platform labour are so far either ignored as a topic in research on gig work, or they are framed as an allocative instrument only. This article argues that working time regimes instead have both a coordinating and controlling effect. Adopting the analytical framework of labour process theory, the article hence focuses on the interrelation of working time and control regimes. The empirical material presented stems from research on platform-based food courier work in Germany and is based on a mixed methods research design consisting of interviews, multi-sited ethnography and a survey. The findings show that platforms implement hybrid control regimes that are not only based on the sufficiently analysed algorithmic management, but also on complementary control through working time regimes: temporal control. Platforms organise intra-platform markets where workers compete for shifts by means of performance. Thus, platforms are able to ensure an efficient and simultaneously reliable use of an autonomous and spatially distributed workforce. Furthermore, it is shown that platform labour is not placeless, either. The effects of its control regime vary according to different local conditions. As a result, platforms cannot be analysed only as techno-cultural ecosystems, but also as local-specific socio-economic structures. [ABSTRACT FROM AUTHOR]

Background: Previous research has shown that teachers hold misconceptions about multimedia learning (e.g., multimedia instruction needs to be adapted to students' learning styles), which may be at odds with evidence‐based teaching. Objectives: Refutation texts are a classical method to reduce misconceptions and thus to stimulate conceptual change. We wanted to know whether making use of a computer algorithm to personalize refutation texts would best initiate teachers' conceptual change. Methods: We designed an online experiment, in which N = 129 in‐service teachers read either (1) expository texts (without direct refutation), (2) common refutation texts, or (3) personalized refutation texts. The teachers filled in a misconception questionnaire pre and post to assess their conceptual change. Results and Conclusions: Statistical analyses revealed that personalized refutation texts initiated the strongest conceptual change, which was driven by increased feelings of guilt and shame. Common refutation texts did not foster teachers' conceptual change as compared to expository texts. These findings indicate that refutation texts should be personalized for experienced practitioners such as teachers. Takeaways: Personalized refutation seems to be promising in the context of online teacher training programs. Further research should test to which extent the present findings also apply to other groups of experienced learners or practitioners. Lay Description: What is already known about this topic?: Teachers hold misconceptions about multimedia learning (e.g., learning materials should be adapted to students' individual learning styles, such as visualizers or verbalizers).Refutation texts, naming a commonly held misconception, disproving it and introducing a scientific explanation, are a common means to reduce misconceptions.Personalization fosters learning by drawing the learner's attention toward the discrepancy between their own beliefs and the learning material, further creating an impasse experience.Said impasse experience may trigger teachers' conceptual change, as, for teachers' conceptual change, a certain degree of discomfort is required. Yet, anger, caused by lessoning teachers on their topic may cause repulse and hamper learning. What this paper adds?: With a computer algorithm, we can efficiently personalize refutation texts by automatically matching them to teachers' answers in a pre‐test. Such a personalized refutation instruction may especially foster conceptual change.Within a randomized experiment, the personalized refutation instruction worked best compared to common refutation texts and expository texts.Feelings of guilt and shame moderated the effect of a personalized refutation, as teachers felt more addressed in their misconceptions and thus experienced the required impasse experience.Feelings of anger did not play an important role within our experiment. The implications of study findings for practitioners: Computer algorithms enable efficient personalization of instruction to better deal with heterogeneous groups of learners (e.g., with big differences in prior knowledge or experience, such as in the case of in‐service teachers).Refutation texts work better for teachers when they are personalized. Common refutation texts do not work better than expository texts.An advantage of digital instruction is the use of algorithms to efficiently personalize instructions even for larger groups. [ABSTRACT FROM AUTHOR]