Etramp5 as a useful serological marker in children to assess the immediate effects of mass drug campaigns for malaria
Bibliographic record
Abstract
INTRODUCTION: Serological methods provide useful metrics to estimate age-specific period prevalence in settings of low malaria transmission; however, evidence on the use of seropositivity as an endpoint remains scarce in studies to evaluate combinations of malaria control measures, especially in children. This study aims to evaluate the immediate effects of a targeted mass drug administration campaign (tMDA) in Haiti by using serological markers. METHODS: The tMDA was implemented in September-October 2018 using sulfadoxine-pyrimethamine and single low-dose primaquine. A natural quasi-experimental study was designed, using a pretest and posttest in a cohort of 754 randomly selected school children, among which 23% reported having received tMDA. Five antigens were selected as outcomes (MSP1-19, AMA-1, Etramp5 antigen 1, HSP40, and GLURP-R0). Posttest was conducted 2-6 weeks after the intervention. RESULTS: At baseline, there was no statistical difference in seroprevalence between the groups of children that were or were not exposed during the posttest. A lower seroprevalence was observed for markers informative of recent exposure (Etramp5 antigen 1, HSP40, and GLURP-R0). Exposure to tMDA was significantly associated with a 50% reduction in the odds of seropositivity for Etramp5 antigen 1 and a 21% reduction in the odds of seropositivity for MSP119. CONCLUSION: Serological markers can be used to evaluate the effects of interventions against malaria on the risk of infection in settings of low transmission. Antibody responses against Etramp5 antigen 1 in Haitian children were reduced in the 2-6 weeks following a tMDA campaign, confirming its usefulness as a short-term marker in child populations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".