Stroke Rehabilitation Clinical Practice Guidelines in Low- and Middle-Income Countries: A Systematic Review of Quality and Unique Features
Bibliographic record
Abstract
INTRODUCTION: Efforts toward reducing stroke burden have been an immense challenge. One important reasons could be the scope and quality of clinical practice guidelines (CPGs) developed for stroke rehabilitation in low- and middle-income countries (LMICs), restricting its translation to clinical practice. This systematic review aimed to assess the availability, scope and quality of CPGs for stroke rehabilitation in LMICs. METHODS: Following PRISMA guidelines, CPGs for stroke rehabilitation in LMICs were searched across four major electronic databases (Medline, Embase, CINAHL, and PEDro). Additional studies were identified from grey literature and a hand search of key bibliographies and search engines. The availability and content of the CPGs were narratively summarized and quality of de novo CPGs was analyzed using "Appraisal of Guidelines REsearch and Evaluation" (AGREE) tools: version II & Recommendations Excellence (REX) version. Features of contextualizations/adaptations of non-de novo CPGs were narratively summarized. RESULTS: Twelve CPGs from 10 countries were included. CPGs from Pakistan, Sri Lanka, India, and China were developed de novo. CPGs from Kenya, Philippines, South Africa, Cameroon, Mongolia, and Ukraine were contextualized/adapted based on existing guidelines from high-income countries. Most contextualized CPGs had limited stakeholder involvement, local health systems/patient pathway analyses. All ten countries included recommendations for physiotherapy, seven for communication, swallowing, and five for occupational therapy services poststroke. Quality assessment using AGREE-REX and AGREE-II for de novo guidelines was poor, especially scoring low in development and applicability. CONCLUSION: Contextualized CPGs for stroke rehabilitation in LMICs were scarcely available and not meeting required quality. There is a need for development of context-specific, culturally relevant CPGs for stroke rehabilitation in LMICs to improve implementation/translation into clinical practice.
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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 | 0.070 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.021 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".