Contributing factors to surgical site infection after tibial plateau leveling osteotomy: A follow‐up retrospective study
Bibliographic record
Abstract
OBJECTIVE: To identify factors associated with surgical site infection (SSI) after tibial plateau leveling osteotomy (TPLO). STUDY DESIGN: Retrospective case series. ANIMALS: Dogs (n = 541) that underwent TPLO (n = 659). METHODS: Medical records of dogs that underwent TPLO from 2011-2018 were reviewed. Data collected included perioperative and postoperative antimicrobial administration, stifle inspection, duration of surgery and anesthesia, comorbidities, and development of SSI including timing, microbiological investigation, and implant removal. Referring veterinarians were contacted for all dogs without a recorded return visit. Risk factors for SSI were assessed by using a multivariable logistic regression model built by using a stepwise approach. RESULTS: Surgical site infection was documented in 71 of 659 (11%) TPLO, with methicillin-resistant Staphylococcus pseudintermedius accounting for 20 of 71 (28%) infections. Protective factors against SSI included administration of postoperative antimicrobials (odds ratio [OR] 0.263; 95% CI = 0.157, 0.442) and timing of preoperative antimicrobial administration. Preoperative antimicrobial timing was protective against SSI when it was administered more than 60 minutes before the first incision compared with administration within 30 minutes (OR 0.275; 95% CI = 0.112, 0.676) or within 60 minutes (OR 0.419; 95% CI = 0.189, 0.929) of the first incision. CONCLUSION: Early administration of perioperative antimicrobials and postoperative antimicrobial administration were protective against SSI after TPLO. CLINICAL SIGNIFICANCE: Antimicrobials can influence the risk of SSI after TPLO. Perioperative and postoperative antimicrobial administration timing should be considered to reduce 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".