Risk Factors for Antibiotic-Resistant Escherichia coli Carriage in Young Children in Peru: Community-Based Cross-Sectional Prevalence Study
Bibliographic record
Abstract
Few studies have examined the influence of individual-, household-, and community-scale risk factors on carriage of resistant commensal bacteria. We determined children's medical, agricultural, and environmental exposures by household, pharmacy, and health facility surveys and Escherichia coli cultures of children, mothers' hands, household animals, and market chickens in Peru. Among 522 children with a positive stool culture, by log-binomial regression, using "any antibiotic" and 1-14 (versus 0) sulfa doses in the past 3 months increased children's risk, respectively, for ampicillin- and sulfamethoxazole-resistant E. coli carriage (P = 0.01-0.02). Each household member taking "any antibiotic" increased children's risk for sulfamethoxazole- and multidrug-resistant E. coli carriage (P < 0.0001). Residence in a zone where a larger proportion of households served home-raised chicken (as contrasted with intensively antibiotic-raised market chicken) protected against carrying E. coli resistant to all drugs (P = 0.0004-0.04). Environmental contamination with drug-resistant bacteria appeared to significantly contribute to children's carriage of antibiotic-resistant E. coli.
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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 | 0.001 |
| 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".