P.055 Does age matter in the CaRMS neurology match?
Bibliographic record
Abstract
Background: The Canadian Resident Matching Services (CaRMS) collects comprehensive data on residency applicants. However, match outcomes by age were not reported. It was unclear whether older applicants found it more difficult to match to the specialties of their choice, i.e. does age influence match? We ask in particular, does age affect the neurology match? Methods: In response to written request, CaRMS provided pre-pandemic age data for 2015-2019 inclusive, divided into group 1 (30 or younger) and group 2 (31-40 inclusive). Results: In 2019, 39 of the 69 group 1 and 6 of the 23 group 2 neurology applicants were matched into neurology (odds ratio (OR)=2.2|p=0.01). In contrast, urology (OR=6|p=0.001) had the worst odds and family medicine (OR=1.2|p=0.002) had the best odds for older applicants in 2019. Average OR (2015-2019) was 1.6 for neurology, 3.1 for urology, 1.3 for family medicine, and between 1.3 and 3.1 for nearly all other specialties. Conclusions: Older neurology applicants were less likely to match than younger peers while match probability was statistically significantly lower in nearly all specialties for older applicants.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 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".