Global Neurosurgery at the University of Toronto: Past and Present Efforts, and a Charter for the Future
Bibliographic record
Abstract
Kenneth McKenzie arrived in Toronto in 1923, bringing with him the legacy of being the first neurosurgeon in Canada. Since then,Toronto has established itself as the hub of Canadian neurosurgery, in both volumes of cases, the strength of trainees, as well asresearch output (1). As one of the largest training programs in North America (2), Toronto has had ongoing international connections,chiefly through the fellowship programs within our division. However, to our recollection,the earliest instance in which Torontodemonstrated a concerted effort towards the formal work in global neurosurgery was through the persistent and continued efforts ofAb Guha (1957-2009), who amongst many philanthropic activities, establish the National Neuroscience Institute in Calcutta (India), hiscity of birth, as his goal. Since then, interest in global neurosurgery has remained strong within our division, with multiple continuedand consistent collaboration areas. These include Mark Bernstein’s travels within Africa and SouthEast Asia, expanding the reach ofawake craniotomies; James Rutka’s efforts to strengthen local surgeons throughout Ukraine; George Ibrahim’s collaborations in Haiti toexpand the surgical treatment of pediatric neurosurgical conditions; and MojganHodaie’s work on structured curricula for neurosurgeryresidents. Simultaneously, Toronto neurosurgery has focused on encouraging fellows from low- and middle-income countries (LMIC’s)to join our center, in many cases funded by the first Chair in International Neurosurgery (3).
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".