Surgical site infection definitions consensus: a first step toward improving prevention in veterinary medicine
Bibliographic record
Abstract
Objective: To establish specific veterinary surgical site infection (SSI) terminology to support the creation of consistent, comparable, and repeatable clinical and research datasets. Methods: Establishment of SSI definitions by iterative Delphi questionnaires leading to a convergence of consensus opinion by a multidisciplinary panel of 32 specialists in large- and small-animal surgery (European College of Veterinary Surgeons, American College of Veterinary Surgeons), veterinary internal medicine (American College of Veterinary Internal Medicine, European College of Veterinary Internal Medicine, European College of Equine Internal Medicine), anesthesia (European College of Veterinary Anesthesia and Analgesia), critical care (American College of Veterinary Emergency and Critical Care, European College of Veterinary Emergency and Critical Care), dentistry (European Veterinary Dental College), microbiology, preventive medicine (American College of Veterinary Preventive Medicine), animal welfare (European College of Animal Welfare and Behavioural Medicine), and human infection control. Consensus was defined as agreement by a minimum of 75% of panel members. Results: The panel defined 18 terms for veterinary use, including those for superficial, deep, and organ/space infections; surgical procedure; pyrexia; wound classification and closure; and agreements on SSI monitoring timeframes. Conclusions: A common clinical and research language appropriate to the veterinary field useable in future SSI surveillance practice has been established through expert consensus. Clinical Relevance: The use of a standard SSI language in veterinary practice is central to the future reliability of studies, their comparison, and the understanding of clinical risk factors in the development and prevention of SSI.
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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 | 0.607 | 0.587 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.011 | 0.036 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".