A prospective cohort of patients with common scabies treated with 10% benzyl benzoate emulsion as monotherapy: EPIGALE study
Bibliographic record
Abstract
BACKGROUND: In addition to general measures, pharmacological treatment is the basis of the management of scabies. No recent data in real-life are available on the efficacy and safety of 10% benzyl benzoate emulsion for skin application administered as monotherapy. METHOD: This prospective, multicenter, French observational study comprised a registry and a prospective cohort with a follow-up at 28 days and a telephone call at week 12. To participate in the registry, patients had to be over 1 month old, ambulatory, presenting common, nonhyperkeratotic, untreated scabies. To be included in the cohort, patients had to be included in the registry and treated with two applications of 10% benzyl benzoate emulsion 8 days apart. The primary endpoint was cure at day 28. RESULTS: Of the 186 patients included in the registry, 116 were included in the cohort. Fourteen patients were included in the cohort without being included in the registry, which led to a total of 130 patients in the cohort. At day 28, 119/130 (91.5%; 95% CI 85.4-95.6%) were clinically cured. The cure was confirmed by dermoscopy in 44/47 patients (93%). Among the 130 patients, the cure rate was 82% at week 12. Of the 119 patients cured at day 28, the rate of cure at week 12 was 89.9%. CONCLUSION: In real life, two applications of 10% benzyl benzoate emulsion 8 days apart provides high cure rates in patients with common scabies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".