Retail Meat Consumption and the Acquisition of Antimicrobial Resistant <i>Escherichia coli</i> Causing Urinary Tract Infections: A Case–Control Study
Bibliographic record
Abstract
BACKGROUND: The increasing incidence of community-acquired urinary tract infections (UTIs) caused by antimicrobial resistant Escherichia coli, and observations of potential outbreaks of UTI-causing E. coli, suggest that food may be an important source of E. coli in women who develop UTI. We sought to determine if acquisition of and infection with a UTI-causing, antimicrobial resistant E. coli isolate is associated with a woman's dietary habits, specifically her preparation and consumption of retail meat products. METHODS: Between April 2003 and June 2004, a case-control study was conducted. The dietary habits of women with UTI caused by an antimicrobial resistant E. coli (cases) and women with UTI caused by fully susceptible E. coli (controls) were compared. Broth microdilution was used to perform antimicrobial resistance testing. All E. coli isolates were genotyped by the pulsed-field gel electrophoresis (PFGE) method. RESULTS: Ninety-nine women met study criteria. Women who were infected with multidrug-resistant E. coli reported more frequent chicken consumption (adjusted OR = 3.7, 95% CI 1.1, 12.4). Women with UTI caused by an ampicillin- or cephalosporin-resistant E. coli isolate reported more frequent consumption of pork (adjusted OR = 3.2, 95% CI 1.0, 10.3 and adjusted OR = 4.0, 95% CI 1.0, 15.5, respectively). Frequent alcohol consumption was associated with antimicrobial resistant UTI. CONCLUSIONS: This study provides epidemiologic evidence that antimicrobial resistant, UTI-causing E. coli could have a food reservoir, possibly in poultry or pork.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".