Original algorithms for the detection of cardiovascular involvement of neglected tropical diseases
Bibliographic record
Abstract
INTRODUCTION: Neglected tropical diseases (NTDs) introduce considerable morbidity and mortality on a global scale, directly impacting over 1 billion individuals as well as their families and communities. Afflicted individuals may have limited access to resources and care in these regions, contributing to a high proportion of chronic, progressive, and systemic disease. The cardiovascular system is at particular risk of demise for several NTDs, yet remains largely unstudied due in part to the lack of robust data collection mechanisms in the most impacted regions. AREAS COVERED: The present review is a part of the Neglected Tropical Diseases and other Infectious Diseases affecting the Heart (NET-Heart) Project, aiming at summarizing the current knowledge on cardiovascular implications of NTDs and providing diagnostic as well as management recommendations which can be tailored to low-resource settings. The diagnostic and management algorithms of 13 unique NTDs are presented and summarized. EXPERT OPINION: Recognizing cardiac manifestations of NTDs can significantly alter disease trajectory and all physicians benefit from improved knowledge about NTDs. Great potential exists to advance patient care by improving data collection, communication, and international collaboration.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".