Canadian stroke best practice recommendations: Secondary prevention of stroke, sixth edition practice guidelines, update 2017
Bibliographic record
Abstract
The 2017 update of The Canadian Stroke Best Practice Recommendations for the Secondary Prevention of Stroke is a collection of current evidence-based recommendations intended for use by clinicians across a wide range of settings. The goal is to provide guidance for the prevention of ischemic stroke recurrence through the identification and management of modifiable vascular risk factors. Recommendations include those related to diagnostic testing, diet and lifestyle, smoking, hypertension, hyperlipidemia, diabetes, antiplatelet and anticoagulant therapies, carotid artery disease, atrial fibrillation, and other cardiac conditions. Notable changes in this sixth edition include the development of core elements for delivering secondary stroke prevention services, the addition of a section on cervical artery dissection, new recommendations regarding the management of patent foramen ovale, and the removal of the recommendations on management of sleep apnea. The Canadian Stroke Best Practice Recommendations include a range of supporting materials such as implementation resources to facilitate the adoption of evidence to practice, and related performance measures to enable monitoring of uptake and effectiveness of the recommendations. The guidelines further emphasize the need for a systems approach to stroke care, involving an interprofessional team, with access to specialists regardless of patient location, and the need to overcome geographic barriers to ensure equity in access within a universal health care system.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".