Analysis of stress fractures associated with lameness in <scp>T</scp> horoughbred flat racehorses training on different track surfaces undergoing nuclear scintigraphic examination
Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: There is limited information regarding the impact of training track surface on the occurrence of stress fractures. OBJECTIVES: To evaluate the impact of training track surface on the proportion of long bone and pelvic stress fractures associated with lameness in Thoroughbred horses in flat race training undergoing nuclear scintigraphic examination. STUDY DESIGN: Retrospective study. METHODS: Scintigraphic examinations of Thoroughbred flat racehorses were evaluated from 2 hospitals (hospital A [Toronto Equine Hospital], 2003-2009, and hospital B [George D. Widener Hospital for Large Animals, School of Veterinary Medicine, University of Pennsylvania], 1994-2006). Horses admitted to hospital A trained at a single track, at which the main training surface changed from dirt to synthetic on 27 August 2006. Two distinct populations existed at hospital B: horses that trained on dirt (numerous trainers) and those that trained on turf (single trainer). All scintigraphic images were evaluated by a blinded reviewer. Fisher's exact test and logistic regression were used when appropriate, and significance was set at P<0.05. RESULTS: When reviewing 528 scintigraphic examinations from hospital A (257 dirt and 271 synthetic, numerous trainers), there was a greater proportion of stress fractures detected in scintigraphic examinations from horses training on a synthetic surface (31.7%) in comparison to scintigraphic examinations from horses training on a dirt surface (23.0%) at an earlier point in time (P = 0.03). There was a greater proportion of hindlimb/pelvic and tibial stress fractures diagnosed in horses from the synthetic surface-trained group than from the dirt-trained group at hospital A (P<0.04 and P = 0.03, respectively). CONCLUSIONS: This study provides evidence that training surface may affect the proportion of stress fractures diagnosed, but other factors, such as training philosophy, appear to be important. Future prospective investigations to fully elucidate the relationship between training track surface and the proportion of stress fractures and other nonfatal musculoskeletal injuries are warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".