Identifying Existing Evidence to Potentially Develop a Machine Learning Diagnostic Algorithm for Cough in Primary Care Settings: Scoping Review.

BACKGROUND: Primary care is known to be one of the most complex health care settings because of the high number of theoretically possible diagnoses. Therefore, the process of clinical decision-making in primary care includes complex analytical and nonanalytical factors such as gut feelings and dealing with uncertainties. Artificial intelligence is also mandated to offer support in finding valid diagnoses. Nevertheless, to translate some aspects of what occurs during a consultation into a machine-based diagnostic algorithm, the probabilities for the underlying diagnoses (odds ratios) need to be determined. OBJECTIVE: Cough is one of the most common reasons for a consultation in general practice, the core discipline in primary care. The aim of this scoping review was to identify the available data on cough as a predictor of various diagnoses encountered in general practice. In the context of an ongoing project, we reflect on this database as a possible basis for a machine-based diagnostic algorithm. Furthermore, we discuss the applicability of such an algorithm against the background of the specifics of general practice. METHODS: The PubMed, Scopus, Web of Science, and Cochrane Library databases were searched with defined search terms, supplemented by the search for gray literature via the German Journal of Family Medicine until April 20, 2023. The inclusion criterion was the explicit analysis of cough as a predictor of any conceivable disease. Exclusion criteria were articles that did not provide original study results, articles in languages other than English or German, and articles that did not mention cough as a diagnostic predictor. RESULTS: In total, 1458 records were identified for screening, of which 35 articles met our inclusion criteria. Most of the results (11/35, 31%) were found for chronic obstructive pulmonary disease. The others were distributed among the diagnoses of asthma or unspecified obstructive airway disease, various infectious diseases, bronchogenic carcinoma, dyspepsia or gastroesophageal reflux disease, and adverse effects of angiotensin-converting enzyme inhibitors. Positive odds ratios were found for cough as a predictor of chronic obstructive pulmonary disease, influenza, COVID-19 infections, and bronchial carcinoma, whereas the results for cough as a predictor of asthma and other nonspecified obstructive airway diseases were inconsistent. CONCLUSIONS: Reliable data on cough as a predictor of various diagnoses encountered in general practice are scarce. The example of cough does not provide a sufficient database to contribute odds to a machine learning-based diagnostic algorithm in a meaningful way.

Medical students' coping with stress and its predictors: a cross-sectional study.

Objectives: To analyse stress coping styles of medical students at different time points of medical education and to identify predictors of functional coping. Methods: A cross-sectional study was conducted among medical students (N = 497, 361 women and 136 men) before year one (n = 141), after year one (n = 135) and after year five (n = 220). Students answered the Brief Coping Orientation to Problems Experienced Inventory, the Work-Related Behaviour and Experience Patterns, the Perceived Medical School Stress Instrument and the Maslach Burnout Inventory. Multiple regression was used to examine factors associated with functional coping. Results: Single factor ANOVA indicated a significant difference for functional coping between the time points (F (2, 494) = 9.52, p < .01), with fifth-year students scoring significantly higher than students before or after year one. There was a significant difference in dysfunctional coping (F (2, 494) = 12.37, p < .01), with students before year one and after year five scoring higher than those after year one. Efficacy (ß = 0.15, t (213) = 4.66, p < .01), emotional distancing (ß = 0.04, t (213) = 3.50, p < .01) and satisfaction with life (ß = 0.06, t (213) = 4.87, p < .01) were positive predictors of functional coping. Conclusions: Scores for both functional and dysfunctional coping vary during medical education. The reasons for low coping scores after year one require further explanation. These findings represent a starting point for investigations into how to promote functional coping during early medical education.