<i>Canadian Stroke Best Practice Recommendations</i> : Mood, Cognition and Fatigue following Stroke, 6th edition update 2019
Bibliographic record
Abstract
The 2019 update of the Canadian Stroke Best Practice Recommendations (CSBPR) for Mood, Cognition and Fatigue following Stroke is a comprehensive set of evidence-based guidelines addressing three important issues that can negatively impact the lives of people who have had a stroke. These include post-stroke depression and anxiety, vascular cognitive impairment, and post-stroke fatigue. Following stroke, approximately 20% to 50% of all persons may be affected by at least one of these conditions. There may also be overlap between conditions, particularly fatigue and depression. If not recognized and treated in a timely matter, these conditions can lead to worse long-term outcomes. The theme of this edition of the CSBPR is Partnerships and Collaborations, which stresses the importance of integration and coordination across the healthcare system to ensure timely and seamless care to optimize recovery and outcomes. Accordingly, these recommendations place strong emphasis on the importance of timely screening and assessments, and timely and adequate initiation of treatment across care settings. Ideally, when screening is suggestive of a mood or cognition issue, patients and families should be referred for in-depth assessment by healthcare providers with expertise in these areas. As the complexity of patients treated for stroke increases, continuity of care and strong communication among healthcare professionals, and between members of the healthcare team and the patient and their family is an even bigger imperative, as stressed throughout the recommendations, as they are critical elements to ensure smooth transitions from acute care to active rehabilitation and reintegration into their community.
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 | 0.008 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.028 | 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".