Canadian Stroke Best Practice Recommendations
Bibliographic record
Abstract
ABSTRACT: The seventh edition of the Canadian Stroke Best Practice Recommendations for Rehabilitation and Recovery following Stroke includes a new section devoted to the provision of virtual stroke rehabilitation. This consensus statement uses Grading of Recommendations, Assessment, Development and Evaluations methodology and Appraisal of Guidelines for Research & Evaluation II principles. A literature search was conducted using PubMed, Embase, and Cochrane databases. An expert writing group reviewed all evidence and developed recommendations, as well as consensus-based clinical considerations where evidence was insufficient for a recommendation. All recommendations underwent internal and external review. These recommendations apply to hospital, ambulatory care, and community-based settings where virtual stroke rehabilitation is provided. This guidance is relevant to health professionals, people living with stroke, healthcare administrators, and funders. Recommendations address issues of access, eligibility, consent and privacy, technology and planning, training and competency (for healthcare providers, patients and their families), assessment, service delivery, and evaluation. Virtual stroke rehabilitation has been shown to safely and effectively increase access to rehabilitation therapies and care providers, and uptake of these recommendations should be a priority in rehabilitation settings. They are key drivers of access to high-quality evidence-based stroke care regardless of geographical location and personal circumstances in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.160 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.013 | 0.006 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.066 | 0.021 |
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".