COVID-19: Implications for People with Chagas Disease
Bibliographic record
Abstract
As the global COVID-19 pandemic advances, it increasingly impacts the vulnerable populations who already bear a heavy burden of neglected tropical diseases. Chagas disease (CD), a neglected parasitic infection, is of particular concern because of its potential to cause cardiac, gastrointestinal, and other complications which could increase susceptibility to COVID-19. The over one million people worldwide with chronic Chagas cardiomyopathy require special consideration because of COVID-19’s potential impact on the heart, yet the pandemic also affects treatment provision to people with acute or chronic indeterminate CD. In this document, a follow-up to the WHF-IASC Roadmap on CD, we assess the implications of coinfection with SARS-CoV-2 and Trypanosoma cruzi, the etiological agent of CD. Based on the limited evidence available, we provide preliminary guidance for testing, treatment, and management of patients affected by both diseases, while highlighting emerging healthcare access challenges and future research needs.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".