Adverse reactions to the ivermectin treatment of onchocerciasis patients: does infection with the human immunodeficiency virus play a role?
Bibliographic record
Abstract
To assess and compare the adverse effects resulting from ivermectin treatment of onchocerciasis patients with and without infection with human immunodeficiency virus (HIV-1), 1256 Ugandan cases of onchocerciasis were investigated as they were treated for the first time with the drug. Treatment followed the protocol of the Mectizan Expert Committee (i.e. a single dose of 150 mug/kg body weight). Adverse reactions to the ivermectin were determined, within 48 h of treatment, through questioning and clinical examinations during house-to-house visits. The HIV-1 status of each patient aged >15 years was initially determined using indirect ELISA, and any ELISA-positives were then confirmed in a western-blot assay. Among the cases aged >15 years, the frequency of adverse reactions to ivermectin was higher among those seropositive for HIV-1 (53.4%) than among the seronegative (45.7%) but the difference was not statistically significant (P = 0.25). The severity of the adverse reactions observed was, however, significantly lower in the HIV-1-positive patients than in the seronegative patients, with median scores of 1.37 and 1.68, respectively (P = 0.044). The conclusion is that ivermectin can be safely used for mass treatment in areas where the prevalences of onchocerciasis and HIV-1 infection are both high.
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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 | 0.001 | 0.004 |
| 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) | 0.001 | 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".