A Comparison of Short-Term Osteoarthritis Progression and Long-Term Clinical Outcome in Relation to the Extend of Meniscal Damage and Subsequent Removal in Dogs with Cranial Cruciate Ligament Rupture
Bibliographic record
Abstract
Introduction : Meniscal tears are common in dogs with cranial cruciate ligament rupture (CCLR) and removal of the damaged part changes the contact biomechanics of the joint. The objective of this study was to compare the short-term postoperative progression of osteoarthritis (OA) and long-term clinical outcome in dogs with intact menisci (NT), hemi (HM)- and partial meniscectomies (PM). We hypothesized that dogs with HM or PM have greater progression of OA and higher LOAD disability scores compared to NT. Materials and Methods : Medical records from 2015 to 2022 were searched for dogs undergoing surgery for CCLR. The stifles were divided into three groups, NT, PM, and HM. Preoperative and 8 weeks postoperative radiographs were scored for OA. The scoring was performed by one observer who was blinded to the extent of meniscal damage. The difference in OA scores was compared with a t -test or Wilcoxon signed-pairs rank test depending on distribution. Results : At the time of writing 132 stifles met the inclusion criteria and 26 owners had responded with the LOAD score. There was no significant difference in OA progression between the groups ( p > 0.44). The caudal tibial plateau had significant OA progression for PM ( p < 0.0001) and HM ( p 0.0322) but not for NT ( p 0.2986). Discussion/Conclusion : OA progression is seen in stifles within 8 weeks after TPLO surgery. Removal of meniscal parts worsens OA locally but overall OA progression is not significantly affected by the extend of meniscectomy. Acknowledgements : There was no proprietary interest or funding provided for this project. Publication History Article published online: 11 September 2023 © 2023. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.000 |
| 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".