Valvular surgery for rheumatic heart disease in Africa: a scoping review protocol
Bibliographic record
Abstract
Background: Rheumatic heart disease (RHD) remains a major health challenge in Africa, where the prevalence is notably high. Valvular surgery is a crucial procedure for managing severe RHD. However, the current state and historical trend of research on this subject in African populations is not well understood. Understanding the scope, subject matter, and quality of the literature on this topic over time is essential to inform future research and clinical practice. Objective: This paper aims to assess the current published literature to evaluate vital outcomes such as surgical outcomes, survival rates, postoperative complications, long-term quality of life, morbidities, mortalities, and barriers to valve surgery for patients with RHD in Africa. Methods: This protocol adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). An extensive search will be conducted across different databases, including PubMed, Scopus, Web of Science, Cochrane Library, African Index Medicus, and African Journals Online. Studies will be identified through keyword searches and will be reviewed against predefined inclusion and exclusion criteria by two reviewers, with a third reviewer resolving any discrepancies. A narrative synthesis will be conducted to describe the findings. Conclusion: The findings from this scoping review will provide an understanding of the current literature on valvular surgery for RHD in African contexts. This will help guide future research directions in this field.
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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 | 0.080 | 0.067 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.080 | 0.012 |
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".