Influence of fixation systems on complications after tibial plateau leveling osteotomy in dogs greater than 45.4 kilograms (100 lb)
Bibliographic record
Abstract
OBJECTIVE: To determine the influence of 3 fixation systems on complications rate after tibial plateau leveling osteotomy (TPLO) in dogs >45.4 kg. STUDY DESIGN: Retrospective case series. SAMPLE POPULATION: Dogs (N = 287, 342 stifles) >45.4 kg with cranial cruciate ligament tear treated with TPLO. METHODS: The medical records of dogs treated with TPLO were reviewed for fixation and postoperative complications, with a follow-up of at least 6 weeks. A random effects logistic regression model was used to evaluate the association between the type of TPLO fixation system and complications. RESULTS: The fixation systems included a 3.5-mm broad TPLO plate alone (8P; 78.4%), a 3.5-mm broad TPLO plate with SOP (String of Pearls) plate (8AP; 14.9%), and a 3.5-mm standard TPLO plate (6P; 6.7%). Among the included stifles, 214 (62.6%) fixation systems were classified as locking, and 128 (37.4%) were classified as nonlocking. The fixation system was predictive of complications. The 8P had the lowest odds ratio for complication among the 3 fixation systems. Odds of developing complications were higher with the 8AP fixation system than with the 8P fixation system. Locking fixation eliminated the association between weight and complication rate. CONCLUSION: Fixation of a TPLO with the 8AP increased the risk of complications compared with the 8P in this population of large dogs. CLINICAL SIGNIFICANCE: Locking fixation of TPLO with a 3.5-mm broad TPLO plate alone should be considered in large dogs because it may reduce complications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".