Ruling out early trimester pregnancy when implementing community-based deworming programs
Bibliographic record
Abstract
BACKGROUND: Large-scale deworming programs have, to date, mostly targeted preschool- and school-age children. As community-based deworming programs become more common, deworming will be offered to women of reproductive age. The World Health Organization recommends preventive chemotherapy be administered to pregnant women only after the first trimester. It is therefore important for deworming programs to be able to identify women in early pregnancy. Our objective was to validate a short questionnaire which could be used by deworming program managers to identify and screen out women in early pregnancy. METHODOLOGY/PRINCIPAL FINDINGS: In May and June 2018, interviewers administered a questionnaire, followed by a pregnancy test, to 1,203 adult women living in the Peruvian Amazon. Regression analyses were performed to identify questions with high predictive properties (using the pregnancy test as the gold standard). Test parameters were computed at different decision tree nodes (where nodes represented questions). With 106 women confirmed to be pregnant, the positive predictive value of asking the single question 'Are you pregnant?' was 100%, at a 'cost' of a false negative rate of 1.9% (i.e. 21 women were incorrectly identified as not pregnant when they were truly pregnant). Additional questions reduced the false negative rate, but increased the false positive rate. Rates were dependent on both the combination and the order of questions. CONCLUSIONS/SIGNIFICANCE: To identify women in early pregnancy when deworming programs are community-based, both the number and order of questions are important. The local context and cultural acceptability of different questions should inform this decision. When numbers are manageable and resources are available, pregnancy tests can be considered at different decision tree nodes to confirm pregnancy status. Trade-offs in terms of efficiency and misclassification rates will need to be considered to optimize deworming coverage in women of reproductive age.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".