Extended spectrum β lactamase-producing Enterobacteriaceae shedding by race horses in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: We aimed to investigate the prevalence, molecular epidemiology and prevalence factors for Extended Spectrum β-Lactamase-producing Enterobacteriaceae (ESBL-E) shedding by race horses. A cross-sectional study was performed involving fecal samples collected from 169 Thoroughbred horses that were housed at a large racing facility in Ontario, Canada. Samples were enriched, plated on selective plates, sub-cultured to obtain pure cultures and ESBL production was confirmed. Bacterial species were identified and antibiotic susceptibility profiles were assessed. E. coli sequence types (ST) and ESBL genes were determined using multilocus sequence type (MLST) and sequencing. Whole genome sequencing was performed to isolates harboring CTX-M-1 gene. Medical records were reviewed and associations were investigated. RESULTS: Adult horses (n = 169), originating from 16 different barns, were sampled. ESBL-E shedding rate was 12% (n = 21/169, 95% CI 8-18%); 22 ESBL-E isolates were molecularly studied (one horse had two isolates). The main species was E. coli (91%) and the major ESBL gene was CTX-M-1 (54.5%). Ten different E. coli STs were identified. Sixty-four percent of total isolates were defined as multi-drug resistant. ESBL-E shedding horses originated from 8/16 different barns; whereas 48% (10/21) of them originated from one specific barn. Overall, antibiotic treatment in the previous month was found as a prevalence factor for ESBL-E shedding (p = 0.016, prevalence OR = 27.72, 95% CI 1.845-416.555). CONCLUSIONS: Our findings demonstrate the potential diverse reservoir of ESBL-E in Thoroughbred race horses. Multi-drug resistant bacteria should be further investigated to improve antibiotic treatment regimens and equine welfare.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".