Evaluation of Osteotomy Healing in Boxer Dogs Undergoing Tibial Plateau Levelling Osteotomy Using Two Radiographic Scoring Systems
Bibliographic record
Abstract
Abstract Objective The aim of this study was to (1) compare 5-point and 10-point bone healing radiographic scoring systems using postoperative tibial plateau levelling osteotomy (TPLO) radiographs and (2) determine whether Boxer osteotomy healing time differs from age-matched Labrador Retrievers. Study Design This was a multicentre retrospective study. Fifty-eight client-owned dogs undergoing TPLO (29 Boxers and 29 Labrador Retrievers) were included. Five board-certified surgeons evaluated the radiographs three independent times. Osteotomy healing approximately 8 weeks postoperatively was graded using previously reported 5-point and 10-point scoring systems and immediate postoperative radiographs were assessed for the presence of an osteotomy gap of ≥1 mm. Results Both scoring systems had good consistency among observers. Intraobserver consistency was good in three out of five observers using the 5-point system and in four out of five observers using the 10-point system. Boxers had significantly lower radiographic healing scores at 8 weeks postoperatively compared with Labrador Retrievers using both scoring systems (p ≤ 0.001). The presence of an osteotomy gap postoperatively resulted in significantly lower healing scores at 8 weeks using both scoring systems (p < 0.001) in both breeds. Conclusion No difference was found in inter- and intraobserver variability between scoring systems. Boxer dogs had lower healing scores at 8 weeks after TPLO compared with Labrador Retrievers. An osteotomy gap of ≥1 mm was associated with lower healing scores.
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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 | 0.004 | 0.005 |
| 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 | 0.000 | 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".