Subchondral Bone Disease, Treatments, and Outcomes in Nonracing Horses
Bibliographic record
Abstract
Introduction: Subchondral bone disease is frequently diagnosed on MRI, but no standardized treatment protocol exists. Our objectives were to review subchondral bone disease lesions and treatments in a population of horses referred for MRI and to assess outcomes (soundness and return to work). Materials and Methods: MRI (1.5 T) reports from 2014 to 2023 and follow-up information from clients and referring veterinarians were reviewed. Lesions were categorized as either Grade 0 (margin irregularity), 1 (sclerosis/minimal bone loss), or 2 (bone loss/STIR hyperintensity). Treatments were grouped into rest/shoeing changes, conservative management more than rest/shoeing (e.g., injections), and surgical treatment. Data was analysed using chi-square analysis with significance at p ≤ 0.05. Results: Eighty-seven subchondral bone lesions (67 limbs and 41 horses) were evaluated, with only the primary lame limb included for analysis. Follow-up was obtained in 36/41 cases, with 20 cases having bone lesions as the primary cause of lameness. No significant differences in outcomes were found between treatments (rest/shoeing changes 80% [4/5] sound and 100% [5/5] returned to work; conservative management 75% [6/8] sound and returned to work; surgical treatment 86% [6/7] sound and returned to work) or lesion grade. Compared with the number that returned to work (85%), horses were less likely (47%) to return at the same level or higher than they were performing pretreatment ( p = 0.01). Discussion/Conclusion: Treatment choice appeared to depend on the severity or type of lesion. Limitations include variation of lesion severity within grades. As long as some targeted treatment is provided for the diagnosed subchondral lesions, most horses appear to improve. Acknowledgment The study was funded through the University of Tennessee Center of Excellence Summer Veterinary Scholars Program. Publication History Article published online: 15 July 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".