Best practice guidelines for stroke in Cameroon: An innovative and participatory knowledge translation project
Bibliographic record
Abstract
BACKGROUND: Although the adherence to stroke guidelines in high-income countries has been shown to be associated with improved patient outcomes, the research, development and implementation of rehabilitation related guidelines in African countries is lacking. OBJECTIVES: The purpose of this article is to describe how a group of front-line practitioners collaborated with academics and students to develop best practice guidelines (BPG) for the management and rehabilitation of stroke in adult patients in Cameroon. METHOD: A working group was established and adapted internationally recognised processes for the development of best practice guidelines. The group determined the scope of the guidelines, documented current practices, and critically appraised evidence to develop guidelines relevant to the Cameroon context. RESULTS: The primary result of this project is best practice guidelines which provided an overview of the provision of stroke rehabilitation services in the region, and made 83 practice recommendations to improve these services. We also report on the successes and challenges encountered during the process, and the working group's recommendations aimed at encouraging others to consider similar projects. CONCLUSION: This project demonstrated that there is interest and capacity for improving stroke rehabilitation practices and for stroke guideline development in Africa.
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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 | 0.101 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".