Bibliographic record
Abstract
OBJECTIVE: In 2010, Pain Medicine was formally recognized as a subspecialty in Canada by the Royal College of Physicians and Surgeons of Canada, a national organization with oversight of the medical education of specialists in Canada. The first trainees began their training at the Western University, London, Canada in July, 2014. This article traces the process of Pain Medicine's development as a discipline in Canada and outlines its multiple entry routes, 2-year curriculum, and assessment procedures. DESIGN: The application for specialty status was initiated in 2007 with the understanding that while Anesthesiology would be the parent specialty, the curriculum would train clinicians in a multidisciplinary setting. To receive recognition as a Royal College subspecialty, Pain Medicine had to successfully pass through three phases, each stage requiring formal approval by the Committee on Specialties. The multiple entry routes to this 2-year subspecialty program are described in this article as are the objectives of training, the curriculum, assessment of competency and the practice-eligibility route to certification. The process of accreditation of new training programs across Canada is also discussed. CONCLUSIONS: The new Pain Medicine training program in Canada will train experts in the prevention, diagnosis, treatment and rehabilitation of the spectrum of acute pain, cancer pain and non-cancer pain problems. These physicians will become leaders in education, research, advocacy and administration of this emerging field.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".