Neuropathology in Canada: Overview of Development and Current Status
Bibliographic record
Abstract
BACKGROUND: The expansion of neurosurgery and neurology in Montreal and Toronto in the early 20th century was th stimulus for the development of neuropathology in Canada. Rooted in the disciplines of the neurosciences and laboratory medicine, neuropathology evolved into an independent discipline with the founding of the Canadian Association of Neuropathologists in 1960, and the recognition as a specialty by the Royal College of Physicians and Surgeons in Canada in 1965, fostering the development of several successful training programs. Nonetheless, a paucity of data remains concerning the background of training, scopes of practice, and career paths. METHOD: We conducted a survey of all physicians in Canada who have either practiced neuropathology or undergone relevant training. RESULTS: In 2009, 53 physicians were engaged in the practice of neuropathology, either exclusively or a substantial proportion of their time. Most work in tertiary hospitals, but a few service non-academic medical centers. Three routes of training were identified: direct from medical school (often with relevant research training), indirect from another clinical neuroscience specialty, and following or in conjunction with certification in one of the other pathology specialties. Practice profiles differ slightly, and some of the neuropathologists entering from pathology have mixed anatomical pathology/neuropathology responsibilities. Many of those with prior exposure in the neurosciences are more productive with regard to research and publications. CONCLUSIONS: Existing multiple options for neuropathology training have facilitated recruitment and allowed development of a mosaic of specialists able to fulfill the diversity of needs in Canadian medical and scientific communities.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.028 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".