Meniscal tears in horses: an evaluation of clinical signs and arthroscopic treatment of 80 cases
Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: There is little published information available describing clinical signs, arthroscopic findings and prognosis of meniscal injuries in horses. OBJECTIVES: To evaluate the effect on the outcome not only of the arthroscopic findings and treatment, but also of the clinical and radiographic signs in these horses. METHODS: The following were recorded for each case: the meniscal injury, graded according to severity; clinical and radiographic findings prior to surgery; any concurrent injury in the joint seen at arthroscopy. The effect of these factors and the grade of injury on the outcome were analysed using Fisher's exact test or Chi-square analysis. Only horses whose meniscal injury was judged to be the primary cause of lameness were included in the series. RESULTS: A series of 80 meniscal injuries were diagnosed and treated arthroscopically by the authors at the Liphook Equine Hospital and 47% of horses returned to full use. Statistically, poor prognosis was associated with increasing severity of the meniscal injury, the presence of concurrent articular cartilage lesions and radiographic abnormalities in the joint. Arthroscopic treatment of many lesions was limited by the inaccessibility of parts of the femorotibial joint. POTENTIAL RELEVANCE: Further work is required to improve and evaluate arthroscopic techniques for the treatment of these injuries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".