Consensus Statement: The Development of a National Canadian Migraine Strategy
Bibliographic record
Abstract
BACKGROUND: Migraine is a significant cause of suffering and disability in the Canadian population, and imposes a major cost on Canadian Society. Based on current medical science, much more could be done to provide better comprehensive medical care to the millions of individuals with migraine in Canada. OBJECTIVE: To propose and design a national Canadian Migraine Strategy which could be implemented to reduce migraine related disability in Canada. METHODS: A multidisciplinary task force of the Canadian Headache Society met for a Canadian Migraine Summit Meeting in Halifax, Nova Scotia in June, 2009. Pertinent literature was reviewed and a consensus document was produced based upon the round table discussion at the meeting. RESULTS: The outline of a national Canadian Migraine Strategy was created. This strategy is based on the chronic disease management model, and would include: an outline of what constitutes appropriate migraine care for Canadians, educational programs (for health care professionals, individuals with migraine, and the general public), research programs, and the development of the necessary organizations and partnerships to develop further and implement the Canadian Migraine Strategy. CONCLUSIONS: Based upon the medical literature and expert discussion at the meeting, a national Canadian Migraine Strategy with a patient self-management focus has the potential to improve patient care and reduce headache related disability in Canada.
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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 | 0.064 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".