William D. Stevenson: Atlantic Canada's first neurosurgeon.
Bibliographic record
Abstract
The origins of neurosurgical services in Atlantic Canada are tied to the individual efforts of William D. Stevenson. Born in Hamilton, Ontario, Stevenson completed his senior matriculation in Dunnville, Ontario, before studying medicine at the University of Toronto. He completed the Gallie surgical course in Toronto and then spent 1 year training with Edward Archibald at McGill University. After working for 2 years with the Canadian Mobile Neurosurgical Unit in Europe during the Second World War, Stevenson undertook formal neurosurgical training with Kenneth G. McKenzie, Canada's first neurosurgeon. Stevenson was thereafter recruited to Halifax to start the neurosurgical service at the Victoria General Hospital in January 1948, and he remained head of the division for the next 26 years. His pioneering work laid the foundations for the establishment of a major academic neurosurgical service at Dalhousie University and was crucial for the establishment of neurosurgery in Atlantic Canada. After his retirement, Stevenson moved back to Ontario and began his second career, transferring his passion for neurosurgery to oil painting. His legacy to neurosurgery in Atlantic Canada will be remembered in perpetuity with the annual Neurosurgery Resident Research Award at Dalhousie University, established and named in his honour. This paper focuses on Stevenson's life and work in neurosurgery as Atlantic Canada's first neurosurgeon.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.106 | 0.024 |
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".