C.05 Canadian neurosurgery operative landscape
Bibliographic record
Abstract
Background: The Canadian Neurosurgery Research Collaborative (CNRC) is a trainee-led multi-centre collaboration made up of representatives from 12 of 14 neurosurgical centres with residency programs. To demonstrate the potential of this collaborative network, we gathered administrative operative data from each centre in order to provide a snapshot of the operative landscape in Canadian neurosurgery. Methods: Residents from each training program provided adult neurosurgical operative data for the 2014 calendar year, including the number of surgeries in the subcategories cranial, spinal, and peripheral nerve. Because some residency programs have surgeries distributed among more than one hospital, we calculated mean case load per residency program and per hospital. Results: Interim results from 6 neurosurgery residency programs are presented (with data from other programs forthcoming). Overall, there were on average 2,352 operative cases per residency program (n=6) and 1,176 operative cases per adult hospital (n=12). Among 5 programs with more detailed operative data, the mean numbers of cranial, spinal, peripheral nerve, and miscellaneous surgeries per residency program were 757 (47%), 487 (30%), 47 (3%), and 319 (20%) respectively. Conclusions: We show as a proof-of-concept that a trainee-led nation-wide research collaborative can generate meaningful data in a Canadian context.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.272 | 0.037 |
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".