Quality of stroke guidelines in low- and middle-income countries: a systematic review
Bibliographic record
Abstract
Objective To identify gaps in national stroke guidelines that could be bridged to enhance the quality of stroke care services in low-and middle-income countries.Methods We systematically searched medical databases and websites of medical societies and contacted international organizations.Country-specific guidelines on care and control of stroke in any language published from 2010 to 2020 were eligible for inclusion.We reviewed each included guideline for coverage of four key components of stroke services (surveillance, prevention, acute care and rehabilitation).We also assessed compliance with the eight Institute of Medicine standards for clinical practice guidelines, the ease of implementation of guidelines and plans for dissemination to target audiences.Findings We reviewed 108 eligible guidelines from 47 countries, including four low-income, 24 middle-income and 19 high-income countries.Globally, fewer of the guidelines covered primary stroke prevention compared with other components of care, with none recommending surveillance.Guidelines on stroke in low-and middle-income countries fell short of the required standards for guideline development; breadth of target audience; coverage of the four components of stroke services; and adaptation to socioeconomic context.Fewer low-and middle-income country guidelines demonstrated transparency than those from high-income countries.Less than a quarter of guidelines encompassed detailed implementation plans and socioeconomic considerations.Conclusion Guidelines on stroke in low-and middle-income countries need to be developed in conjunction with a wider category of health-care providers and stakeholders, with a full spectrum of translatable, context-appropriate interventions.
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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 | 0.028 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".