Acupuncture has potential in managing axial stiffness in steeplechase racehorses: a blinded prospective randomized preliminary study
Bibliographic record
Abstract
OBJECTIVE: Evaluate the short-term effects of acupuncture on the dynamic manifestations of axial stiffness in steeplechase racehorses. ANIMALS: 12 steeplechase racehorses presenting signs of axial stiffness during training. METHODS: Horses were randomly assigned to either an acupuncture treatment by an experienced certified acupuncturist (n = 6) or no treatment as negative controls (6). The horses' locomotion was evaluated during training before treatment (D0) and 7 (D7) and 14 (D14) days after by their rider and trainer through a questionnaire. Additionally, the improvement of their dorsal flexibility 2 days after treatment was evaluated subjectively at the trot, free jumping at the canter was evaluated by expert clinicians, and free jumping at the trot was evaluated objectively via inertial measurement units. RESULTS: Significantly more horses were improved on D7 and D14 in the acupuncture group (6/6) compared with the control group (1/5; P =.01) according to the scores set by the trainer and riders. Subjective evaluation of the dorsal flexibility also revealed a significant improvement (P = .04) for horses receiving the acupuncture treatment (median improvement score, 0.50 [reference range, 0.5 to 0.9]) compared with control horses (-0.25 [reference range, -0.5 to 0]). CLINICAL RELEVANCE: Acupuncture may be an interesting nondoping strategy to improve clinical signs of axial stiffness and performance on steeplechase racehorses.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".