Prevalence of Rheumatic Heart Disease and Other Cardiac Conditions in Low-Risk Pregnancies in Kenya: A Prospective Echocardiography Screening Study
Bibliographic record
Abstract
Background: Rheumatic heart disease (RHD) in sub-Saharan Africa contributes to significant cardiac morbidity and mortality, yet prevalence estimates of RHD lesions in pregnancy are lacking. Objectives: Our first aim was to evaluate women using echocardiography to estimate the prevalence of RHD and other cardiac lesions in low-risk pregnancies. Our second aim was to assess the feasibility of screening echocardiography and its acceptability to patients. Methods: We prospectively recruited 601 pregnant women from a low-risk antenatal clinic at a tertiary care maternity centre in Western Kenya. Women completed a questionnaire about past medical history and cardiac symptoms. They underwent standardized screening echocardiography to evaluate RHD and non-RHD associated cardiac lesions. Our primary outcome was RHD-associated cardiac lesions and our secondary outcome was a composite of any clinically-relevant cardiac lesion or echocardiography finding. We also recorded duration of screening echocardiography and its acceptability among pregnant women in this sample. Results: The point prevalence of RHD-associated cardiac lesions was 5.0/1,000 (95% confidence interval: 1.0-14.5), and the point prevalence of all clinically significant lesions/findings was 21.6/1,000 (11.6-36.7). Mean screening time was seven minutes (SD 1.7, range: 4-17) for women without cardiac abnormalities and 13 minutes (SD 4.6, range: 6-23) for women with abnormal findings. Echocardiography was acceptable to women with 74.2% agreeing to participate. Conclusions: The prevalence of clinically-relevant cardiac lesions was moderately high in a low-risk population of pregnant women in Western Kenya.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".