Bibliographic record
Abstract
OBJECTIVE: Determine if distributed practice of of neurological exam (NE) skills in first year medical school produces sustained improvements in the skills of second year students. METHODS: A prospective, controlled, non-blinded study conducted at McGill University (class size = 180 students). Expanded teaching of muscle stretch reflexes was provided to first year medical students. A structured examination of muscle stretch reflexes (max score = 100) was administered in second year medical school after a required two week rotation in Neurology. Results for class A (received the intervention in first year) were compared to the results for the preceding class B (had not received the intervention). RESULTS: 77 of 177 (44%) eligible in class A and 69 of 166 (42%) eligible students in class B participated. Results were analyzed separately for each of the two examiners. Mean (SD) scores were 95.2 (5.6) for class A (intervention) and 81.7 (11.1) for class B (control) for the first examiner and 90.4 (8.2) for class A and 83.8 (11.7) for class B for the second examiner. Results were statistically significant (Mann-Whitney test z = 5.27, p<0.0001 first examiner and z = 2.67, p<0.0038 second examiner). CONCLUSIONS: Distributed practice of muscle stretch reflexes during first year medical school results in improved performance by second year medical students after their mandatory clinical rotation in neurology, even when examined up to 14 months after the intervention. This finding has implications for the teaching of the NE.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".