Trends in entry to RCPSC neurosurgery residency training through the CaRMS match since loss of eligibility for ABNS certification
Bibliographic record
Abstract
Background: After July 16, 1997, Royal College of Physicians and Surgeons of Canada (RCPSC) trainees in neurosurgery were no longer eligible for American Board of Neurological Surgeons (ABNS) certification. It was anticipated that this would lead to an influx of neurosurgeons in Canada. Methods: We analyzed historical Canadian Residency Matching Service (CaRMS) data for 1997–2014 for trends in neurosurgery residency positions offered, vacancy rates, resident demographics and other pertinent data. Results: A mean of 0.94% of medical students applied to neurosurgery as their first choice (range: 0.54%-1.79%). Comparing 2 consecutive time periods (1997–2005 vs. 2006–2014), the mean number of neurosurgery entry positions per year increased from 14 to 19, while mean applicant numbers increased from 24 to 28, respectively. Ninety-five percent of those accepted into neurosurgery ranked it as their first choice discipline and few candidates who ranked neurosurgery highest were unmatched. Women applying to neurosurgery as their first choice discipline were equally likely to match as men (84% vs. 85%) and comprised 28% of neurosurgery residents selected since 2008 (vs. 14% in 1997–2007). Conclusions: The number of neurosurgery CaRMS positions and applicants have increased since 1997. This will have implications for neurosurgical workforce planning and physician employment in Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".