Deliberate practice of diagnostic clinical reasoning reveals low performance and improvement of diagnostic justification in pre-clerkship students
Bibliographic record
Abstract
PURPOSE: Diagnostic errors are a large burden on patient safety and improving clinical reasoning (CR) education could contribute to reducing these errors. To this end, calls have been made to implement CR training as early as the first year of medical school. However, much is still unknown about pre-clerkship students' reasoning processes. The current study aimed to observe how pre-clerkship students use clinical information during the diagnostic process. METHODS: In a prospective observational study, pre-clerkship medical students completed 10-11 self-directed online simulated CR diagnostic cases. CR skills assessed included: creation of the differential diagnosis (Ddx), diagnostic justification (DxJ), ordering investigations, and identifying the most probable diagnosis. Student performances were compared to expert-created scorecards and students received detailed individualized formative feedback for every case. RESULTS: 121 of 133 (91%) first- and second-year medical students consented to the research project. Students scored much lower for DxJ compared to scores obtained for creation of the Ddx, ordering tests, and identifying the correct diagnosis, (30-48% lower, p < 0.001). Specifically, students underutilized physical exam data (p < 0.001) and underutilized data that decreased the probability of incorrect diagnoses (p < 0.001). We observed that DxJ scores increased 40% after 10-11 practice cases (p < 0.001). CONCLUSIONS: We implemented deliberate practice with formative feedback for CR starting in the first year of medical school. Students underperformed in DxJ, particularly with analyzing the physical exam data and pertinent negative data. We observed significant improvement in DxJ performance with increased practice.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".