Epidemiology of cutaneous leishmaniasis in central <scp>A</scp>mazonia: a comparison of sex‐biased incidence among rural settlers and field biologists
Bibliographic record
Abstract
OBJECTIVE: Cutaneous leishmaniasis (CL) is more frequently reported in men than in women; this may be due to male-biased exposure to CL vectors, female-biased resistance against the disease or both. We sought to determine whether gender-specific exposure to vector habitats explains male-biased CL incidence in two human populations of central Amazonia. METHODS: We compared the CL incidence in one population of field researchers (N = 166), with similar exposure for males and females, and one population of rural settlers (N = 646), where exposure is overall male-biased. We used a combination of questionnaires and clinical data to quantify CL cases, and modelled disease incidence in a Bayesian framework. RESULTS: There was a moderately higher incidence of CL among men than among women in both populations, but male bias decreased as exposure time increased. Disease incidence was overall higher among field researchers, suggesting that they are an important but understudied CL risk group. CONCLUSION: Our comparison of two contrasting populations provided epidemiological evidence that CL incidence can be male-biased even when exposure is comparable in both sexes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".