Neuropathology in Canada: Overview of Development and Current Status
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it