Raisonnement clinique et simulation : faciliter la priorisation d’hypothèses grâce aux patients simulés. Données d’une recherche quantitative
Bibliographic record
Abstract
Background: Prioritizing diagnostic hypotheses can be difficult for novice medical students given their limited clinical exposure. Simulated clinical reasoning (CR) clinics allow students to practice focused histories with a simulated patient (SP). The delivery of clinical data by SPs can influence hypothesis generation. Objective: This pilot study seeks to test whether the transmission of key elements through SP acting influences CR prioritization among medical students. Method: The diagnostic hypotheses of two cohorts of students of the same academic level were compared following a virtual interview with an SP. The SPs in the experimental group were given a targeted script and briefing on key elements while the SPs in the control group were given a traditional script and briefing. The difference between the distributions of frequencies of the hypotheses of the two groups was determined using the chi-square calculation. Results: The students in the experimental group prioritized expert-validated hypotheses more than those in the control group. The control group showed greater variability in their diagnostic choices. Conclusion: Targeting the delivery of key elements by SPs could be a way to help novice medical students prioritize their diagnostic hypotheses. Simulated CR clinics therefore become a space for learning about CR in the absence of clinical exposure. The risk of inducing premature closure of clinical reasoning needs further research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".