Identifying sub-optimal responses to ivermectin in the treatment of River Blindness
Bibliographic record
Abstract
Identification of drug resistance before it becomes a public health concern requires a clear distinction between what constitutes a normal and a suboptimal treatment response. A novel method of analyzing drug efficacy studies in human helminthiases is proposed and used to investigate recent claims of atypical responses to ivermectin in the treatment of River Blindness. The variability in the rate at which Onchocerca volvulus microfilariae repopulate host's skin following ivermectin treatment is quantified using an individual-based onchocerciasis mathematical model. The model estimates a single skin repopulation rate for every host sampled, allowing reports of suboptimal responses to be statistically compared with responses from populations with no prior exposure to ivermectin. Statistically faster rates of skin repopulation were observed in 3 Ghanaian villages (treated 12-17 times), despite the wide variability in repopulation rates observed in ivermectin-naïve populations. Another village previously thought to have high rates of skin repopulation was shown to be indistinguishable from the normal treatment response. The model is used to generate testable hypotheses to identify whether atypical rates of skin repopulation by microfilariae could result from low treatment coverage alone or provide evidence of decreased ivermectin efficacy. Further work linking phenotypic poor responses to treatment with parasite molecular genetics markers will be required to confirm drug resistance. Limitations of the skin-snipping method for estimating parasite load indicates that changes in the distribution of microfilarial repopulation rates, rather than their absolute values, maybe a more sensitive indicator of emerging ivermectin resistance.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".