P.086 A randomized trial of a simple intervention to improve neurosurgery rotation experience for senior medical students
Bibliographic record
Abstract
Background: High volumes, ill patients, and steep learning curves can make neurosurgical rotations challenging for medical students. Furthermore, existing rotations often lack neurosurgery-specific orientation materials and level-appropriate pre-reading resources reducing the educational yield of short rotations. This is compounded by the lack of mandatory neurosurgical rotations across medical schools. We hypothesized that a “Neurosurgery Clerkship Manual” covering key orientation, knowledge, and practical topics would enhance educational experiences and generate sustained knowledge retention. Methods: Students rotating through neurosurgery at three hospitals were randomized to receive(intervention) or not receive(control) free access to the manual before their rotation. Participants completed surveys before, immediately after, and 4-weeks after the rotation assessing expectations, experiences, and clinically-relevant knowledge. Results: 61 participants were randomized between 2014 and 2017 with 43(70.5%) completing all three questionnaires. Baseline demographics, characteristics, and experiences were not significantly different. Those receiving the manual reported increased rotation enjoyment(p=0.02), decreased stress levels (p=0.05), and a greater feeling of being “part of the team”(p=0.01). There were also reductions in feeling like they were “not learning” (p=0.01). Finally, those receiving the manual demonstrated significantly better knowledge after the rotation (91.6%vs80.9%;p=0.04) which was sustained at 4-weeks post-rotation (89.2%vs79.0%;p=0.05). Conclusions: A simple and inexpensive clerkship manual can improve the neurosurgery rotation experience and knowledge retention for medical students.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".