Mosaic arthroplasty in equine stifle and fetlock joints: A retrospective study of 31 cases between 1998 and 2023
Bibliographic record
Abstract
OBJECTIVE: To evaluate the clinical application of equine mosaic arthroplasty for joint surface repair, including outcomes and complications. STUDY DESIGN: Retrospective clinical study. ANIMALS: A total of 31 horses diagnosed with subchondral bone cysts (SBCs) in the femoral condyle (22/31), distal metacarpus (7/31), or metatarsus (2/31). METHODS: Medical records of horses that underwent autologous or allogeneic osteochondral graft transplantation were reviewed. Follow-up lasted at least 12 months. Success was determined in terms of improvements in lameness and post-surgical athletic performance, classified as successful, satisfactory, or unsatisfactory. RESULTS: In total, 68% (21/31) of horses regained soundness and resumed athletic performance at the same or higher level than before surgery. Furthermore, 22% (7/31) and 10% (3/31) exhibited satisfactory and unsatisfactory results, respectively. Seven horses underwent follow-up arthroscopy to treat complications or residual lameness. Among horses with femoral condyle SBCs, 68% (15/22) achieved successful outcomes, compared with 67% (6/9) of those with fetlock SBCs. Age (≤3 vs. >3 years) did not appear to influence outcomes in stifle cases. Horses receiving fewer implanted grafts showed a tendency toward better recovery. CONCLUSION: Mosaic arthroplasty improved lameness in 90% of this mixed-age equine population, with 68% regaining soundness and successfully returning to athletic performance. Unlike other techniques reporting success primarily in 2- and 3-year-old horses, this method could provide an effective surgical alternative for both young and mature horses with SBCs. CLINICAL SIGNIFICANCE: Mosaic arthroplasty may serve as a viable surgical option for managing SBCs.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".