133 Neurosurgery Education in The Medical School Curriculum: A Scoping Review
Bibliographic record
Abstract
Abstract Introduction Worldwide, there is no specific medical school curriculum in neurosurgery despite a high burden of neurosurgical disease that is often assessed, investigated and managed by generalists. This scoping review was carried out to map available evidence pertaining to the provision of neurosurgery education in the medical school curriculum across the world. Method This review was conducted in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews. Results Ten studies were included. Six were from the United Kingdom, two from the United States, and one each from Canada and Ireland. Two studies evaluated perceptions of both medical students and practicing clinicians, five studies evaluated the perceptions of medical students and three studies reported perceptions of clinicians only. Three main themes were identified. Neurosurgery was perceived as an important part of the general medical student curriculum. Exposure to neurosurgery teaching was varied but when received, deemed useful and students were keen to receive more. Interest in a neurosurgical career amongst medical students was high. Conclusions There is a lack of a specialty-specific medical school curriculum and variability of medical students’ exposure to neurosurgery teaching exists. Our findings highlight the need to systematically assess specialty-specific teaching and determine adequacy.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".