Canadian Stroke Best Practice Recommendations: Acute Stroke Management, 7<sup>th</sup> Edition Practice Guidelines Update, 2022
Bibliographic record
Abstract
ABSTRACT: The 2022 update of the Canadian Stroke Best Practice Recommendations (CSBPR) for Acute Stroke Management , 7 th edition, is a comprehensive summary of current evidence-based recommendations, appropriate for use by an interdisciplinary team of healthcare providers and system planners caring for persons with an acute stroke or transient ischemic attack. These recommendations are a timely opportunity to reassess current processes to ensure efficient access to acute stroke diagnostics, treatments, and management strategies, proven to reduce mortality and morbidity. The topics covered include prehospital care, emergency department care, intravenous thrombolysis and endovascular thrombectomy (EVT), prevention and management of inhospital complications, vascular risk factor reduction, early rehabilitation, and end-of-life care. These recommendations pertain primarily to an acute ischemic vascular event. Notable changes in the 7 th edition include recommendations pertaining the use of tenecteplase, thrombolysis as a bridging therapy prior to mechanical thrombectomy, dual antiplatelet therapy for stroke prevention, 1 the management of symptomatic intracerebral hemorrhage following thrombolysis, acute stroke imaging, care of patients undergoing EVT, medical assistance in dying, and virtual stroke care. An explicit effort was made to address sex and gender differences wherever possible. The theme of the 7 th edition of the CSBPR is building connections to optimize individual outcomes, recognizing that many people who present with acute stroke often also have multiple comorbid conditions, are medically more complex, and require a coordinated interdisciplinary approach for optimal recovery. Additional materials to support timely implementation and quality monitoring of these recommendations are available at www.strokebestpractices.ca .
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.037 | 0.026 |
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".