Chagas Disease: From Discovery to a Worldwide Health Problem
Bibliographic record
Abstract
Carlos Chagas discovered American trypanosomiasis, also named Chagas disease (CD) in his honor, just over a century ago. He described the clinical aspects of the disease, characterized by its etiological agent (Trypanosoma cruzi) and identified its insect vector. Initially, CD occurred only in Latin America and was considered a silent and poorly visible disease. More recently, CD became a neglected worldwide disease with a high morbimortality rate and substantial social impact, emerging as a significant public health threat. In this context, it is crucial to better understand better the epidemiological scenarios of CD and its transmission dynamics, involving people infected and at risk of infection, diversity of the parasite, vector species and T. cruzi reservoirs. Although efforts have been made by endemic and non-endemic countries to control, treat, and interrupt disease transmission, the cure or complete eradication of CD are still topics of great concern and require global attention. Considering the current scenario of CD, also affecting non-endemic places such as Canada, USA, Europe, Australia and Japan, in this review we aim to describe the spread of CD cases worldwide since its discovery until it has become a global public health concern.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".