Choosing Wisely Neurology: Recommendations for the Canadian Neurological Society
Bibliographic record
Abstract
BACKGROUND: Choosing Wisely Canada (CWC) is a national branch of a global campaign advocating for fewer unnecessary tests and for optimizing patient care. Professional societies representing physicians, pharmacists, and nurses participate by generating lists of recommendations meant to reduce patient harm and resource mismanagement in healthcare. The Canadian Neurological Society (CNS) plays an important role in advocating for quality patient care demonstrated by deriving specific recommendations. This process is described. METHOD: The CNS Choosing Wisely task force adapted 10 recommendations for Canadian neurology practice. These were approved by the CNS board, and subsequently ranked by CNS members. RESULTS: Ten recommendations were brought forward and ranked in a survey completed by CNS members. Survey ranking is presented. The top five recommendations were selected and optimized, resulting in seven key recommendations. CONCLUSION: The recommendations set forth by the CNS will help with resource stewardship and patient safety in the delivery of neurological care by healthcare providers 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 | 0.024 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".