Antimicrobial prophylaxis in companion animal surgery: A scoping review for European Network for Optimization of Antimicrobial Therapy (ENOVAT) guidelines
Bibliographic record
Abstract
Surgical antimicrobial prophylaxis (SAP) is widely used to reduce the risk of surgical site infections (SSI), but there is uncertainty as to what the proportion of SSI reduction is. Therefore, it is difficult for surgeons to properly weigh the costs, risks and benefits for individual patients when deciding on the use of SAP, making it challenging to promote antimicrobial stewardship in primary practice settings. The objective of this study was to map the veterinary evidence focused on assessing the effect of SAP on SSI development and in order to identify surgical procedures with some research evidence and possible knowledge gaps. In October 2021 and December 2022, Scopus, CAB Abstracts, Web of Science Core Collection, Embase and MEDLINE were systematically searched. Double blinded screening of records was performed to identify studies in companion animals that reported on the use of SAP and SSI rates. Comparative data were available from 34 out of 39123 records screened including: eight randomised controlled trials (RCT), 23 cohort studies (seven prospective and 16 retrospective) and three retrospective case series representing 12476 dogs and cats in total. Extracted data described peri- or post-operative SAP in nine, and 25 studies, respectively. In the eight RCTs evaluating SAP in companion animals, surgical procedure coverage was skewed towards orthopaedic stifle surgeries in referral settings and there was large variation in SAP protocols, SSI definitions and follow-up periods. More standardized data collection and agreement of SSI definitions is needed to build stronger evidence for optimized patient care.
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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 | 0.025 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.029 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".