Canadian Stroke Best Practice Recommendations: Managing transitions of care following Stroke, Guidelines Update 2016
Bibliographic record
Abstract
Every year, approximately 62,000 people with stroke and transient ischemic attack are treated in Canadian hospitals. For patients, families and caregivers, this can be a difficult time of adjustment. The 2016 update of the Canadian Managing Transitions of Care following Stroke guideline is a comprehensive summary of current evidence-based and consensus-based recommendations appropriate for use by clinicians who provide care to patients following stroke across a broad range of settings. The focus of these recommendations is on support, education and skills training for patients, families and caregivers; effective discharge planning; interprofessional communication; adaptation in resuming activities of daily living; and transition to long-term care for patients who are unable to return to or remain at home. Unlike other modules contained in the Canadian Stroke Best Practice Recommendations (such as acute inpatient care), many of these recommendations are based on consensus opinion, or evidence level C, highlighting the absence of conventional evidence (i.e. randomized controlled trials) in this area of stroke care. The quality of care transitions between stages and settings may have a direct impact on patient and family outcomes such as coping, readmissions and functional recovery. While many qualitative and non-controlled studies were reviewed, this gap in evidence combined with the fact that mortality from stoke is decreasing and more people are living with the effects of stroke, underscores the need to channel a portion of available research funds to recovery and adaptation following the acute phase of stroke.
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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".