European Network for Optimization of Veterinary Antimicrobial Therapy ( <scp>ENOVAT</scp> ) 2025 guidelines for surgical antimicrobial prophylaxis in dogs and cats
Bibliographic record
Abstract
Surgical antimicrobial prophylaxis involves the administration of antimicrobials to reduce the risk of a surgical site infection and represents a significant proportion of all antimicrobial use in cats and dogs. This evidence-based, European Network for Optimization of Veterinary Antimicrobial Therapy guideline provides recommendations for both peri- and post-operative surgical antimicrobial prophylaxis for a wide range of soft tissue and orthopaedic procedures performed in dogs and cats. A multidisciplinary panel developed the recommendations while adhering to the Grading of Recommendations Assessment, Development and Evaluation framework. The opinions of veterinary practitioners were incorporated to ensure applicability. Ten strong recommendations against, three conditional recommendations against and five conditional recommendations for the use of surgical antimicrobial prophylaxis were drafted by the panel. Strong recommendations against surgical antimicrobial prophylaxis were often informed by low- to very low-certainty evidence that treatment has no beneficial effect. However, the anticipated harmful effects of antimicrobial use are well established and offer an important counterbalance to unsubstantiated use. Conditional recommendations were made when there was a probable balance of effects in one direction, although appreciable uncertainty was present. The European Network for Optimization of Veterinary Antimicrobial Therapy guidelines initiative encourages national or regional guideline makers to use the evidence presented in this document and the supporting systematic review to draft national or local guidance documents that support rational surgical antimicrobial 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 | 0.026 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".