Use of large‐scale veterinary data for the investigation of antimicrobial prescribing practices in equine medicine
Bibliographic record
Abstract
BACKGROUND: As antimicrobial resistant bacterial strains continue to emerge and spread in human and animal populations, understanding prescription practices is key in benchmarking current performance and setting goals. Antimicrobial prescription (AP) in companion veterinary species is widespread, but is neither monitored nor restricted in the USA and Canada. The veterinary use of certain antimicrobial classes is discouraged in some countries, in the hope of preserving efficacy for serious human infections. OBJECTIVES: The aim of this study was to ascertain the rate of prescription of a number of 'reserved' antimicrobials in a first-opinion US and Canadian horse cohort, and identify trends in their empirical use. STUDY DESIGN: Retrospective cohort study. METHODS: A large convenience sample of electronic medical records (2006-2012) was interrogated using text mining to identify enrofloxacin, clarithromycin and ceftiofur prescriptions. Time series analysis and logistic regression were used to identify trends and risk factors for prescription. RESULTS: Prescription of these antimicrobials as a first-line intervention, without culture and sensitivity testing (CST), was common in this population. Enrofloxacin prescriptions were found to increase over the study period, and there was evidence of either a reducing, or static trend in the proportion of reserved APs informed by CST. MAIN LIMITATIONS: Dose adequacy could not be included due to the nature of the data used. CONCLUSIONS: Empirical use of reserved antimicrobials was common in this population, and further advice and guidance should be issued to first-opinion veterinarians to safeguard antimicrobial efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".