Current British veterinary attitudes to the use of perioperative antimicrobials in small animal surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it