Evaluating Current Screening Programs for Congenital Chagas Disease in Rural Latin America
Bibliographic record
Abstract
Background: Chagas disease (CD), caused by the parasite Trypanosoma cruzi , is endemic in countries across Latin America (1). Vertical transmission from mother to fetus leading to congenital Chagas disease is an increasing concern worldwide due to the often asymptomatic nature of the disease (2,3). If left undetected, congenital CD can lead to cardiac or intestinal complications later on in life (4,5). Identifying congenital CD early leads to almost 100% cure rates, avoiding the progression to chronic CD (5). Detection of congenital CD is especially difficult in rural populations, attributing to poor infrastructure, insufficient equipment, and lack of education. Objectives: This paper will evaluate the effectiveness of current screening techniques available to detect congenital Chagas disease and recommend which program should be implemented in rural settings lacking equipped laboratories and properly trained personnel. Methods: Six available screening techniques to diagnose congenital Chagas disease were evaluated using two frameworks developed by the World Health Organization. Components from the Principles and Practice of Screening for Disease and the ASSURED criteria were combined to identify the most suitable diagnostic tool for use in low-resource, rural areas (6,7). Results: Immunochromatographic (IC) tests matched the criteria set out by the framework. It has high sensitivity, high positive predictive value and high negative predictive value for both whole blood and serum samples. The test is low-cost, simple to administer and provides easy to read rapid results. No refrigeration is necessary, and the test does not require the use of a fully equipped laboratory or highly trained personnel8. Discussion/Conclusions: Although IC tests closely fit the criteria, no studies indicate the use of the test for early diagnosis in newborns, restricting it to use in mothers only. IC tests need to be coupled with an inexpensive, reliable and easy to use test for infants within one month following birth. The evaluation proposes the Chunap test as a promising candidate, with further research to decrease costs and technical requirements9. Ongoing efforts need to be directed toward one test that is able to diagnose both mothers and newborns in a timely manner.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".