Canadian Stroke Best Practice Recommendations: Vascular cognitive impairment, 7th edition practice guidelines update, 2024
Bibliographic record
Abstract
The Canadian Stroke Best Practice Recommendations (CSPR) 7th edition includes this new module on the diagnosis and management of vascular cognitive impairment (VCI) with or without neurodegenerative disease. An expert writing group and people with VCI lived experience (PWLE) reviewed current evidence. Existing recommendations were reviewed and revised, and new recommendations added. Sections include definitions, signs and symptoms, screening, assessment, diagnosis, pharmacological and non-pharmacological management, secondary prevention, rehabilitation, and end-of-life care. PWLE were actively involved in all aspects of the development, ensuring their experiences are integrated. A unique VCI journey map, developed by PWLE, is included, and helped to motivate and anchor the recommendations. We encourage it to be displayed across healthcare settings to raise awareness and support persons with VCI. These VCI CSBPRs emphasize the need for integrated multidisciplinary care across the continuum. Evidence for the diagnosis and management of VCI continues to emerge and gaps in knowledge should drive future research. HIGHLIGHTS: This Canadian Stroke Best Practice Recommendations module focuses specifically on VCI using a structured framework and validated methodology. A comprehensive set of evidence-based recommendations is presented that addresses the continuum from symptom onset to diagnosis, management, and end of life. The recommendations consider individuals who experience VCI because of stroke or because of other vascular pathologies such as atrial fibrillation or heart failure. A journey map of an individual's experience with VCI has been developed by individuals with lived experience. It is a valuable guide to inform educational content, approaches to caring for individuals and families with VCI, and systems planning.
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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".