Prevalence, risk factors, and characterization of multidrug resistant and extended spectrum β-lactamase/AmpC β-lactamase producing <i>Escherichia coli</i> in healthy horses in France in 2015
Bibliographic record
Abstract
BACKGROUND: Although antimicrobial resistance is increasingly common in equine medicine, molecular and epidemiological data remains scarce. OBJECTIVES: We estimated the prevalence of, and risk factors for, shedding of multidrug resistant (MDR), extended spectrum β-lactamase (ESBL)-producing, and AmpC β-lactamase-producing, or some combination of these in Escherichia coli in horses in France. We characterized ESBL/AmpC isolates for antimicrobial susceptibility and the presence of virulence and ESBL/AmpC-associated resistance genes. ANIMALS: Fecal samples from healthy adult horses at 41 premises were collected. A questionnaire was completed by each premises manager. A subset of these samples was tested to build 2 bacterial collections. METHODS: Indicator (without enrichment) and specific (enrichment with ceftriaxone) E. coli tested for antimicrobial susceptibility. Prevalence of isolates nonsusceptible to antimicrobials was estimated at the horse and the premises level. The ESBL/AmpC and virulence genes were identified by PCR. Multivariable logistic regression was used to investigate risk factors for MDR and ESBL/AmpC isolates at premises. RESULTS: . Virulence gene iutA was identified in 1 ESBL/AmpC-producing isolate. Medical treatment, staff numbers, and activity were identified as risk factors for housing horses shedding ESBL/AmpC-producing E. coli isolates. CONCLUSIONS AND CLINICAL IMPORTANCE: Prevalence of healthy horses harboring ESBL/AmpC genes and MDR isolates in their intestinal microbiota is substantial. Risk factors could be used to elaborate guidelines to prevent their dissemination.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".