The Student-Generated Reasoning Tool (SGRT): Linking medical knowledge and clinical reasoning in preclinical education
Bibliographic record
Abstract
The simultaneous integration of knowledge acquisition and development of clinical reasoning in preclinical medical education remains a challenge. To help address this challenge, the authors developed and implemented the Student-Generated Reasoning Tool (SGRT)—a tool asking students to propose and justify pathophysiological hypotheses, generate findings, and critically appraise information. In 2019, students in a first-year preclinical course (n = 171; SGRT group) were assigned to one of 20 teams. Students used the SGRT individually, then in teams, and faculty provided feedback. The control group (n = 168) consisted of students from 2018 who did not use SGRT. Outcomes included academic performance, effectiveness of collaborative environments using the SGRT, and student feedback. Students were five times more likely to get questions correct if they were in the SGRT group versus control group. Accuracy of pathophysiological hypotheses was significantly lower for individuals than teams. Qualitative analysis indicated students benefited from generating their own data, justifying their reasoning, and working individually as well as in teams. This study introduces the SGRT as a potentially engaging, case-based, and collaborative learning method that may help preclinical medical students become aware of their knowledge gaps and integrate their knowledge in basic and clinical sciences in the context of clinical reasoning.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".