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Is Primary Prevention of Rheumatic Fever the Missing Link in the Control of Rheumatic Heart Disease in Africa?

2009· article· en· W2144065854 on OpenAlexaff
Ganesan Karthikeyan, Bongani M. Mayosi

Bibliographic record

VenueCirculation · 2009
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsPopulation Health Research Institute
Fundersnot available
KeywordsMedicineRheumatic feverHeart diseaseIncidence (geometry)Intensive care medicineDiseasevalvular heart diseasePublic healthRheumatic diseaseAcute rheumatic feverPediatricsPhysical therapyInternal medicinePathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.153

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.298
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations94
Published2009
Admission routes1
Has abstractyes

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