Tertiary prevention and treatment of rheumatic heart disease: a National Heart, Lung, and Blood Institute working group summary
Bibliographic record
Abstract
Although entirely preventable, rheumatic heart disease (RHD), a disease of poverty and social disadvantage resulting in high morbidity and mortality, remains an ever-present burden in low-income and middle-income countries (LMICs) and rural, remote, marginalised and disenfranchised populations within high-income countries. In late 2021, the National Heart, Lung, and Blood Institute convened a workshop to explore the current state of science, to identify basic science and clinical research priorities to support RHD eradication efforts worldwide. This was done through the inclusion of multidisciplinary global experts, including cardiovascular and non-cardiovascular specialists as well as health policy and health economics experts, many of whom also represented or closely worked with patient-family organisations and local governments. This report summarises findings from one of the four working groups, the Tertiary Prevention Working Group, that was charged with assessing the management of late complications of RHD, including surgical interventions for patients with RHD. Due to the high prevalence of RHD in LMICs, particular emphasis was made on gaining a better understanding of needs in the field from the perspectives of the patient, community, provider, health system and policy-maker. We outline priorities to support the development, and implementation of accessible, affordable and sustainable interventions in low-resource settings to manage RHD and related complications. These priorities and other interventions need to be adapted to and driven by local contexts and integrated into health systems to best meet the needs of local communities.
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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 | 0.037 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".