Is Primary Prevention of Rheumatic Fever the Missing Link in the Control of Rheumatic Heart Disease in Africa?
Bibliographic record
Abstract
Rheumatic fever and rheumatic heart disease continue to be major public health problems in the developing world, particularly in the countries of sub-Saharan Africa. Because of its cost effectiveness, secondary prophylaxis is advocated as the principal means of disease prevention and control. However, in developing countries, valvular damage, due to earlier, unrecognized episodes of rheumatic fever, has already occurred by the time secondary prophylaxis is instituted. Secondary prophylaxis cannot reduce the incidence of new cases of rheumatic fever and has not been shown to alter the natural history of rheumatic valvular disease. Experience from several regions of the world suggests that incorporation of a strategy of primary antibiotic prophylaxis into a comprehensive program for disease control can reduce the incidence of rheumatic fever and rheumatic heart disease. In this article, we argue that a strategy of primary antibiotic prophylaxis, with appropriate modifications, can be successfully implemented in resource-poor settings across the world and should be a key component of any rheumatic heart disease control program. This, we believe, is essential for reducing the global burden of rheumatic heart disease.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".