Determination of the prevalence and severity of metacarpophalangeal joint osteoarthritis in Thoroughbred racehorses via quantitative macroscopic evaluation
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence and severity of osteoarthritis in the metacarpophalangeal joints of Thoroughbred racehorses via development and validation of a quantitative macroscopic evaluation system. SAMPLE POPULATION: Metacarpophalangeal joints from 50 Thoroughbred racehorses. PROCEDURES: Joints were collected from horses that died or were euthanized within 60 days of racing. Metacarpophalangeal joints were assessed for osteoarthritic degeneration by use of macroscopic and histologic scoring systems, polarized light microscopy, and cartilage biochemical analysis. The global macroscopic score for the entire metacarpophalangeal joint was based on factors that reflected the size and severity of lesions as well as the involvement of weight-bearing surfaces. RESULTS: One-third of all 2- and 3-year-old horses had partial-or full-thickness cartilage lesions and osteoarthritis. Osteoarthritis severity increased until age 6 in this population. Significant correlations were found between macroscopic grade and age, cause of death, glycosaminoglycan depletion, and loss of superficial cartilage zone polarized light intensity. CONCLUSIONS AND CLINICAL RELEVANCE: The macroscopic system devised for this study had good correlations with quantitative methods. Two-and 3-year-old horses had full-thickness cartilage lesions that may have been career limiting. Year-to-year attrition and a small population of older horses may have led to underestimation of the prevalence of osteoarthritis in older horses. The macroscopic scoring system was reliable when used by nonexpert and expert users.
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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 | 0.002 | 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".