Bibliographic record
Abstract
To characterize the professional occupation of Canadian neuropathologists and estimate the future employment demands in neuropathologists, all the active members of the Canadian Association of Neuropathologists in Canada (n=53) were surveyed by E-mail, inquiring as to their estimated date of retirement, their current employment and practice profile, and as to any practice trends they had noticed. 49 members replied: all but one practice at medical school centers. 38 practice exclusively in neuropathology and three of these are employed at less than 75% of a full time equivalent. The remaining practices are mixed neuropathology and anatomical pathology, and one practices exclusively ophthalmic pathology. 35% reported significant neuropathology sub specialization (e.g. forensic, pediatric, neuromuscular). 42% reported greater than 10% of time dedicated to research (of these, median 30%) and 35% greater than 10% time spent in teaching, and 9% greater than 10% time in administration. Of the 49 surveyed, as of the spring of 2019, 14%(seven) of the full time neuropathologists can be expected to retire in the next 10 years, and 6% (three) with mixed AP/NP practices. LEARNING OBJECTIVES This presentation will enable the learner to: 1. Understand the current spectrum of practice of Neuropathologists across Canada 2. Describe the patterns of employment and anticipated retirements of Canadian Neuropathologists
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How this classification was reachedexpand
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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".