Predictive Variables for Complications after TPLO with Stifle Inspection by Arthrotomy in 1000 Consecutive Dogs
Bibliographic record
Abstract
OBJECTIVE: To evaluate risk factors for complications, including meniscal injury and infection, after tibial plateau leveling osteotomy (TPLO) in dogs. STUDY DESIGN: Retrospective case series. SAMPLE POPULATION: Dogs (n=1000; 1146 stifles) with cranial cruciate ligament (CCL) rupture that had TPLO. METHODS: Medical records (January 2004-March 2009) were reviewed for dogs operated sequentially by medial arthrotomy with instrumented meniscal inspection (IMI) and TPLO by a single experienced surgeon. Multiple logistic regression models were used to evaluate independent contribution of risk factors to the recorded complications. RESULTS: Overall complication rate was 14.8%, of which 6.6% were major complications. Incidence of primary meniscal injury (PMI) was 33.2%, and subsequent meniscal injury (SMI) 2.8%. Postoperative infection occurred in 6.6% dogs. Bilateral CCL rupture was diagnosed in 14.6% dogs and no statistically significant complication incidence difference was recorded for simultaneous or staged bilateral surgical procedures. Administration of postoperative antibacterial therapy and being a Labrador reduced infection incidence, whereas increased body-weight and being an intact male increased infection risk. Increased body-weight and complete (versus partial) CCL rupture were significant predictors of overall complications. CONCLUSIONS: Incidence of SMI recorded in this study is similar to that reported previously involving arthroscopic meniscal inspection at time of TPLO. Infection was the single most important complication and antibiotic therapy was protective. Complication rate did not differ between bilateral simultaneous or staged procedures. CLINICAL RELEVANCE: Complication rate after TPLO with arthrotomy and IMI is lower than previously reported, bilateral simultaneous TPLO is reasonable, and incidence of major complications compares favorably with general orthopedic procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".