RAP Sessions ‐ interdisciplinary delivery of anatomy, radiology, and procedural skills (343.2)
Bibliographic record
Abstract
The McMaster University Michael G. DeGroote School of Medicine’s undergraduate medicine curriculum is a concepts‐based curriculum with anatomy sessions, lectures and small group tutorials. Curriculum review revealed a paucity of radiology and procedural skills in the pre‐clerkship training. An interdisciplinary program of anatomy, radiology, and procedural skills was developed. Method: A 3‐hour curriculum was developed that integrated chest anatomy taught by an anatomist, radiology related to the chest taught by a radiologist, and the procedural skill of placing a chest tube by a general surgeon. The session revolved around a case that was part of their regular small group tutorials. Students rated their knowledge of chest anatomy, knowledge of placing a chest tube and their comfort with placing a chest tube pre and post session using a 10‐point Likert rating scale. Students were also asked to rate the experience on a number of dimensions. Results: 23 students participated in this workshop. All completed the pre and post questionnaires. Students reported an increase in self‐rated knowledge of chest anatomy (4.3 vs 6.4), knowledge in placing a chest tube (1.8 vs 7.7), and in comfort with placing a chest tube (1.4 vs 6.3 post. These results were analyzed using Wilcoxon Signed‐Rank Test and the differences were all statistically significant (p<0.001). The sessions were highly rated, with the mean value for organization/venue scale being 9.4 out of 10 (SD=0.6) and educational value scale (effective, appropriate) being 9.3 out of 10 (SD=0.07). Conclusion: RAP sessions are an innovative interdisciplinary curriculum integrating radiology, anatomy and procedural skills within the context of a concept based medical curriculum. Students reported an increase in knowledge and skills and highly rated the experience as a valuable teaching session for concept integration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.111 | 0.028 |
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".