Equine poor performance: the logical, progressive, diagnostic approach to determining the role of the temporomandibular joint
Bibliographic record
Abstract
Abstract Poor performance is an ambiguous term used frequently by people in the horse industry. It means different things to different people, depending on the breed, discipline, or problem being discussed. There are myriad reasons that a horse may fail to achieve the expectations put upon it or, having achieved those goals, begin to falter. Equine temporomandibular joint (TMJ) disease is beginning to be reported as 1 such cause of poor performance. Despite this, in certain disciplines, it has become the trendy diagnosis, and a logical approach to the diagnostic workup is often lacking. Many of the clinical signs attributed to TMJ abnormalities can be readily explained by other more common problems. This ambiguity is compounded by a lack of extensive scientific evidence linking TMJ-related disease to behavioral or performance changes. Despite this fact, the equine TMJ has been reported to be a cause of poor performance, and while rare, it should be included in a differential diagnosis list, albeit one of exclusion. The purpose of this article is to describe a logical, stepwise approach to excluding common causes of poor performance before investigating the potential role of the TMJ in cases of poor performance.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".