Efecto de COVID-19 en el diagnóstico de Chagas congénito en tres departamentos de Bolivia
Bibliographic record
Abstract
Objetivo: Evaluar el impacto de la COVID-19 en el tamizaje y la cobertura diagnóstica de la enfermedad de Chagas en mujeres gestantes y recién nacidos (RN) en tres departamentos de Bolivia. Material y métodos: Estudio cuantitativo, observacional y retrospectivo, de series temporales (unidad mensual, 2018–2022), con comparación prepandemia, pandemia y pospandemia. Se usaron fuentes programáticas departamentales; análisis descriptivo y regresión logística de la “brecha de control” neonatal. Resultados: El tamizaje disminuyó en gestantes y RN en los tres departamentos. Santa Cruz: gestantes 78 966, 68 338 y 45 266; RN 9 666, 8 720 y 4 182. Cochabamba: gestantes 138 175, 106 785 y 58 946; RN 5 482, 3 892 y 1 640. Chuquisaca: gestantes 35 779, 30 597 y 14 414; RN 4 303, 2 836 y 1 246. La positividad materna descendió: Santa Cruz 15,2%, 13,6% y 10,8%; Cochabamba 9,9%, 8,31% y 7,1%; Chuquisaca 21,1%, 16,9% y 14,1%. La transmisión vertical registrada permaneció <3% en los tres contextos. La brecha de control neonatal fue heterogénea: en Santa Cruz se redujo de prepandemia a pandemia y aumentó en pospandemia (OR 2,6; p<0,001); en Cochabamba no hubo cambios significativos; en Chuquisaca se observó un patrón mixto (prepandemia a pandemia OR 1,09; pandemia a pospandemia OR 0,83; p<0,001). Conclusiones: La COVID-19 contrajo la cobertura de tamizaje materno-infantil de forma heterogénea, con brechas pospandemia persistentes. Las bajas tasas de transmisión vertical probablemente reflejan subdiagnóstico. Urge recuperar cobertura, fortalecer la vigilancia y optimizar el algoritmo diagnóstico neonatal.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".