Bibliographic record
Abstract
BACKGROUND: The growing science and technology of various neurosurgical areas fosters subspecialization. The transmission of this expanding knowledge base to the neurosurgical resident becomes an increasing challenge. A survey of neurosurgical residency program directors was undertaken to evaluate their response to the budding subspecialization of spine surgery within general neurosurgery. METHODS: A survey requesting background data, educational infrastructure and prevailing opinion was distributed to all 13 neurosurgical program directors in Canada. The responses were tabulated and results recorded. It is upon these results that conclusions and proposed directions are based. RESULTS/CONCLUSIONS: The current practice of the overwhelming majority of Canadian academic neurosurgical centers is to have neurosurgical spinal subspecialists working under the umbrella of the general neurosurgical division. A large percentage of neurosurgical program directors in Canada believe that the management of spinal disease, including both intradural procedures and instrumentation, is and should remain an integral part of general neurosurgical training. A consensus statement regarding the requirements of neurosurgical training in spinal disorders is the expressed desire of almost all program directors. A proposed direction and resolution is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".