Neglected Tropical Diseases and Other Infectious Diseases Affecting the Heart. The NET-Heart Project: Rationale and Design
Bibliographic record
Abstract
Introduction: Neglected tropical diseases (NTDs) are a group of infections that are prevalent in many of the tropical and sub-tropical developing countries where poverty is rampant. NTDs have remained largely unnoticed in the global health agenda. There is a substantial gap between the burden of disease for NTDs in cardiovascular diseases (CVD) and research devoted to the affected populations. We created a Latin-American initiative with emerging leaders (EL) from the Interamerican Society of Cardiology (IASC) with the objective to perform multiple systematic reviews of NTDs and other infectious diseases affecting the heart: The NET-Heart Project. Objective: To describe the rationale and design considerations of the NET-Heart project. Methods: The NET-Heart Project is a collaborative work of the IASC EL program. The main objective of the NET-Heart project is to systematically evaluate the available evidence of NTDs and other infectious diseases and their cardiovascular involvement. As a secondary objective, this initiative aims to offer recommendations and potential diagnostic and therapeutic algorithms that can aid the management of cardiovascular complications of these infectious diseases. After an expert discussion 17 initial infectious diseases were selected, for each disease we created one working group. The project was structured in different phases: Systematic review, brainstorming workshops, analysis and results, manuscript writing and recommendations and evaluation of clinical implications. Conclusion: The NET-Heart project is an innovative collaborative initiative created to assess burden and impact of NTDs and other infectious diseases in CVD. NTDs can no longer be ignored and must be prioritised on the health and research agenda. This project aims to review in depth the evidence regarding cardiac compromise of these serious conditions and to propose strategies to overcome barriers for efficient diagnosis and treatment of cardiovascular complications.
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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 | 0.247 | 0.266 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".