Retrospective analysis of surgical site infection rates and predisposing factors in clean veterinary surgical procedures
Bibliographic record
Abstract
A Labrador retriever undergoing tibial plateau levelling osteotomy at a university teaching hospital develops purulent discharge from the surgical incision 10 days after what was classified as a clean orthopaedic procedure. This scenario, familiar to every veterinary surgeon, raises the question of how often Surgical Site Infections (SSI) occur in clean veterinary surgeries and what patient, procedural, and environmental factors predispose to them. This research presents a nine-year retrospective analysis (2015-2023) of SSI rates and risk factors in 14,287 clean surgical procedures performed at the Uppsala Veterinary Surgical Research Unit, Sweden. Records from canine (n = 8,416), feline (n = 3,892), and equine (n = 1,979) patients were reviewed for wound classification, surgical duration, American Society of Anesthesiologists (ASA) physical status, implant use, body condition score, patient age, concurrent disease, perioperative antimicrobial prophylaxis, and surgeon experience. SSI was defined according to Centers for Disease Control (CDC) criteria adapted for veterinary use. The overall SSI rate across all species and years was 5.3% (757 of 14,287 procedures). Orthopaedic procedures had the highest rates (7.4% canine, 3.6% feline, 10.8% equine), while clean soft tissue procedures had the lowest (2.1% canine, 1.6% feline, 3.4% equine). Multivariable logistic regression identified six independent risk factors: surgical duration exceeding 90 minutes (OR 3.2, 95% CI 2.7-3.8), implant use (OR 4.1, 95% CI 3.4-4.9), ASA classification of III or above (OR 2.8, 95% CI 2.2-3.5), obesity defined as body condition score above 4 on a 5-point scale (OR 2.1, 95% CI 1.6-2.7), concurrent systemic disease (OR 2.4, 95% CI 1.9-3.0), and age above 10 years (OR 1.9, 95% CI 1.5-2.4). A significant temporal decline in SSI rates was observed from 6.8% in 2015 to 4.5% in 2023 (trend p<0.001), interrupted only by a transient increase during 2020, coinciding with COVID-19-related disruptions to staffing and supply chains. These data provide the largest species-comparative SSI benchmark for clean veterinary surgery and identify modifiable risk factors amenable to targeted prevention strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 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".