Risk factors for and outcomes of noncatastrophic suspensory apparatus injury in Thoroughbred racehorses
Bibliographic record
Abstract
OBJECTIVE: To evaluate effects of toe grabs, exercise intensity, and distance traveled as risk factors for subclinical to mild suspensory apparatus injury (SMSAI) in Thoroughbred racehorses and to compare incidence of severe musculoskeletal injury (MSI) in horses with and without SMSAI. DESIGN: Nested case-control study. ANIMALS: 219 Thoroughbred racehorses racing or in race training. PROCEDURE: Racehorses were examined weekly for 90 days to determine incidence of suspensory ligament injury and monitor horseshoe characteristics. Every horse's exercise speeds and distances were recorded daily. Conditional logistic regression was used to compare exposure variables between incident case (n = 25) and selected control (125) horses. Survival analysis was used to compare time to MSI for horses with (n = 41) and without (76) SMSAI. RESULTS: The best-fitting logistic model for the data included age (< 5 vs > or = 5 years old), toe grab height the week of injury (none vs very low, low, regular, or Quarter Horse height), and weekly distance the week preceding injury (miles). Although the 95% confidence intervals for all odds ratios included 1, the odds for SMSAI appeared to increase with the presence of a toe grab, higher weekly distance, and age > or = 5 years. Horses that had SMSAI were significantly more likely to have a severe MSI or severe suspensory apparatus injury than were horses that did not. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggest that pre-existing SMSAI is associated with development of severe MSI and severe suspensory apparatus injury. Modifying training intensity and toe grab height for horses with SMSAI may decrease the incidence of severe MSI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".