Neuropathology in Canada: The First One Hundred Years
Bibliographic record
Abstract
We describe the evolution of neuropathology in Canada, beginning with William Osler who began working in Montréal in 1874 and finishing with the major period of expansion in the 1970s. Organized services began in the 1930s, in Montréal with the neurosurgeons Wilder Penfield and William Cone, and in Toronto with Eric Linell and Mary Tom, who both began their careers as neuroanatomists. Jerzy Olszewski and Gordon Mathieson, who trained in Montréal and Toronto, drove the creation of the Canadian Association of Neuropathologists in 1960. Training guided by the Royal College of Physicians and Surgeons of Canada was formalized in 1965, with the first certifying examination in 1968 and the subsequent creation of formal structured training programs. The number of neuropathologists in Canada increased rapidly through the 1960s and 1970s, with individuals coming from both clinical neuroscience and anatomic pathology backgrounds, a pattern that persists to the present day.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".