Current British veterinary attitudes to the use of perioperative antimicrobials in small animal surgery
Bibliographic record
Abstract
A questionnaire was sent to 2951 mixed and small animal veterinary practices to examine the use of perioperative antimicrobials in cats and dogs in the UK. The percentage of respondents who always used antimicrobials in two surgical procedures classified according to NRC criteria as 'clean' was 25.3 per cent for removal of a 1 cm cutaneous mass and 32.1 per cent for routine prescrotal castration. Factors considered important in decision-making about when to use antimicrobial agents included immunosuppression, presence of a drain, degree of wound contamination, potential for spillage of visceral contents and implantation of prosthesis. The most common antimicrobial agents mentioned were potentiated amoxicillin (98.0 per cent), amoxicillin (60.5 per cent), clindamycin (21.8 per cent), enrofloxacin (21.7 per cent), cephalexin (18.6 per cent) and metronidazole (12.7 per cent). Forty-three per cent of all responding veterinarians listed a long-acting preparation for perioperative use. The routes used were subcutaneous (76.1 per cent), intravenous (25.8 per cent), intramuscular (19.8 per cent), oral (13.5 per cent) and topical (7.7 per cent). Antimicrobials were given before surgery (66.6 per cent), during surgery (30.2 per cent), immediately after surgery (12.0 per cent) and after surgery (6.3 per cent). This survey has identified the suboptimal use of perioperative antimicrobials in small animal surgery with improvements needed with respect to timing, duration, choice of antimicrobial and a more prudent selection of surgical cases requiring prophylaxis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".