Assessment of accredited veterinary diagnostic laboratory use of breakpoints for canine and feline Escherichia coli infections in the United States and Canada
Bibliographic record
Abstract
The objective of this study was to assess the use of breakpoints in antibiotic susceptibility testing among veterinary diagnostic laboratories in the United States and Canada. An eight-question survey was conducted via phone and email to determine how often laboratories use breakpoints consistent with published guidelines in wounds, lower urinary tract infections and upper urinary tract infections (pyelonephritis) involving Escherichia coli, both in dogs and cats, for a total of 6 different hypothetical clinical scenarios. Nineteen veterinary diagnostic laboratories that perform antibiotic susceptibility testing on samples from dogs and cats in the United States or Canada and were accredited by the American Association of Veterinary Laboratory Diagnosticians (AAVLD) responded to the survey between January 15th and September 15th, 2022. The overall response rate of laboratories that were not excluded for known lack of dog and cat antibiotic susceptibility testing was 19 of 44 laboratories. Of the 17 respondent laboratories that reported using minimal inhibitory concentration breakpoints, only four laboratories used breakpoints consistent with published guidelines in all six clinical scenarios included in the survey. Our results suggest that there is clinically important variation in what breakpoints laboratories use to determine antibiotic susceptibility, which is of antibiotic stewardship and clinical relevance. Using breakpoints that are too high, too low, or inappropriately reporting “not interpreted” as the interpretive category may result in inappropriate use of antibiotics.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".