High Seroprevalence of<i>Leptospira</i>Exposure in Meat Workers in Northern Mexico: A Case-Control Study
Bibliographic record
Abstract
BACKGROUND: The seroepidemiology of Leptospira infection in workers occupationally exposed to raw meat has been poorly studied. This work aimed to determine the association between Leptospira exposure and the occupation of meat worker, and to determine the seroprevalence association with socio-demographic, work, clinical and behavioral characteristics of the meat workers studied. METHODS: We performed a case-control study in 124 meat workers and 124 age- and gender-matched control subjects in Durango City, Mexico. Sera of cases and controls were analyzed for anti-Leptospira IgG antibodies using a commercially available enzyme immunoassay. Data of meat workers were obtained with the aid of a questionnaire. The association of Leptospira exposure with the characteristics of meat workers was analyzed by bivariate and multivariate analyses. RESULTS: Anti-Leptospira IgG antibodies were found in 22 (17.7%) of 124 meat workers and in eight (6.5%) of 124 controls (OR = 3.12; 95% CI: 1.33 - 7.33; P = 0.006). Seroprevalence of Leptospira infection was similar between male butchers (17.6%) and female butchers (18.2%) (P = 1.00). Multivariate analysis of socio-demographic, work and behavioral variables showed that Leptospira exposure was associated with duration in the activity, rural residence, and consumption of snake meat and unwashed raw fruits. CONCLUSIONS: This is the first case-control study of the association of Leptospira exposure with the occupation of meat worker. Results indicate that meat workers represent a risk group for Leptospira exposure. Risk factors for Leptospira exposure found in this study may help in the design of optimal preventive measures against Leptospira infection.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".