Evaluation of the evidence for self-management post-stroke within clinical practice guidelines for people with stroke: A systematic review
Bibliographic record
Abstract
Introduction: Self-management post-stroke is essential where healthcare systems are stretched and stroke prevalence remains high. Self-management is recommended in stroke guidelines however, the quality of guidelines can vary and the evidence underpinning recommendations is unclear. The objectives of this paper are to identify and appraise current stroke guidelines, synthesise self-management recommendations and assess the evidentiary basis of these recommendations. Method: Stroke guidelines were retrieved from a search of four databases and stroke association websites in July 2021. Four independent reviewers assessed their quality using the Appraisal of Guidelines Research and Evaluation 2nd Edition instrument. Self-management recommendations were extracted by hand, evaluated, synthesised and the evidence underpinning them appraised using the Canadian Stroke Best Practice Recommendations framework. Results: Eleven guidelines were included in this systematic review, all of excellent methodological quality overall. One hundred and sixty-one recommendations were extracted from these guidelines and grouped into ten principles of self-management. A quarter of the recommendations were underpinned by level A evidence, 32% level B and 43% level C. Conclusion: Although current stroke guidelines are of high methodological quality, there are considerable inconsistencies in self-management recommendations within and between these guidelines, with varying strengths of underpinning evidence. This article identifies a need for universal consensus regarding evidence-based self-management post-stroke.
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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 | 0.036 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".