Superficial digital flexor tendon lesions in racehorses as a sequela to muscle fatigue: A preliminary study
Bibliographic record
Abstract
REASON FOR PERFORMING STUDY: Racing and training related lesions of the forelimb superficial digital flexor tendon are a common career ending injury to racehorses but aetiology and/or predisposing causes of the injury are not completely understood. OBJECTIVES: Although the injury takes place within the tendon, the lesion must be considered within the context of the function of the complete suspensory system of the distal limb, including the associated muscles. METHODS: Both muscle and tendon function were investigated in vivo using implanted strain gauges in 3 Thoroughbred horses walking, trotting and cantering on a motorised treadmill. These data were combined with assessments of muscle architecture and fibre composition to arrive at an overview of the contribution of each muscle-tendon unit during locomotion. RESULTS: The superficial digital flexor muscle has fatigue-resistant and high force production properties that allow its tendon to store and return elastic energy, predominantly at the trot. As running speed increases, deep digital flexor tendon force increases and it stabilises hyperextension of the fetlock, thus reinforcing the superficial digital flexor in limb load support. The deep digital flexor muscle has fast contracting properties that render it susceptible to fatigue. CONCLUSION: Based on these measurements and supporting evidence from the literature, it is proposed that overloading of the superficial digital flexor tendon results from fatigue of the synergistic, faster contracting deep digital flexor muscle. POTENTIAL RELEVANCE: Future research investigating distal limb system function as a whole should help refine clinical diagnostic procedures and exercise training approaches that will lead to more effective prevention and treatment of digital flexor tendon injuries in equine athletes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".