The optimal timing of post-treatment sampling for the assessment of anthelminthic drug efficacy against Ascaris infections in humans
Bibliographic record
Abstract
The egg reduction rate (ERR) is the current standard mean to assess the efficacy of drugs against human soil-transmitted helminths (STHs; Ascaris lumbricoides, Trichuris trichiura and hookworm). Although the timing of post-treatment sampling is pivotal for a readily interpretation of drug efficacy, there is lack empirical data that allows recommending the optimal time point for a follow-up egg counting. In the present study, we re-analyzed both the kinetics of worm expulsion and egg output for Ascaris lumbricoides following a single oral dose of albendazole in a series of studies previously conducted in Kenyan communities. The results indicate that it takes up to 10 days post-treatment before the expulsion of both adult male and female Ascaris worms is completed, approximately 20% of the worms being expelled between day 7 and 10 post-treatment. The sequential analysis of the egg out put, indicated a poor ERR (89.4%) at day 7 post-treatment, but a 100% ERR at day 14 and 21 post-treatment. Based on our findings we recommend to wait at least 14 days after an albendazole treatment before conducting the follow-up egg count. Any sampling before this time point may result in biased ERR estimates, due the release of residual eggs from moribund or degenerating worms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".