Jockey Falls, Injuries, and Fatalities Associated With Thoroughbred and Quarter Horse Racing in California, 2007-2011
Bibliographic record
Abstract
Background: Despite the popularity of the horse racing industry in the United States and the wide recognition that horse racing is one of the most hazardous occupations, little focused research into the prevention of falls by and injuries to jockeys has been conducted. Purpose: To describe the incidence rates and characteristics of falls and injuries to Thoroughbred and Quarter Horse racing jockeys in the state of California. Study Design: Descriptive epidemiology study. Methods:Data on race-day falls and injuries were extracted from jockey accident reports submitted to the California Horse Racing Board from January 2007 to December 2011. Denominator data, number of jockey race rides, were obtained from commercial and industry databases. Jockey fall, injury, and fatality incidence rates and ratios in Thoroughbred and Quarter Horse flat races were estimated using Poisson regression. Characteristics of falls and injuries are described and compared. Results: In Thoroughbred races, 184 jockey injuries occurred from 360 reported jockey falls, 180,646 race rides, 23,500 races, and 3350 race meetings. In Quarter Horse races, 85 jockey injuries occurred from 145 jockey falls, 46,106 race rides, 6320 races, and 1053 race meetings. Jockey falls occurred at a rate of 1.99 falls per 1000 rides in Thoroughbred races, with 51 % of falls resulting in jockey injury, and 3.14 falls per 1000 rides in Quarter Horse races, with 59 % of falls resulting in jockey injury. The majority of falls occurred during a race, with catastrophic injury or sudden death of the horse reported as the most common cause in both
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".