Relationships between race earnings and horse age, sex, gait, track surface and number of race starts for Thoroughbred and Standardbred racehorses in North America
Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: There is no consensus on objective outcome measures that can be used to determine if a medical or surgical treatment affects race performance. OBJECTIVE: To determine the association between 2 commonly used outcome measures (total starts and total earnings) and age, sex, gait and race surface. METHODS: A cross-sectional study was performed using the race performance data for all Thoroughbred horses age 2, 3, 4 and 5 years racing in the United States, and Standardbred horses of the same ages racing in the United States and Canada during the year 2006. Median earnings and starts were determined for each combination of age, sex and track surface (for Thoroughbred) or gait (for Standardbred). The effect these variables had on starts on race earnings ($) was determined using linear regression. RESULTS: Race records for 68,649 Thoroughbreds and 25,830 Standardbreds were obtained. All independent variables (age, breed, sex, gait, track surface and total number of starts) had a significant impact on total earnings (P<0.0001). CONCLUSIONS: The data show considerable variation across age groups and track surfaces for Thoroughbreds and across age groups for Standardbreds. They also show that the decision to use earnings or starts as outcome measures could have a marked effect on reported success for a particular treatment. POTENTIAL RELEVANCE: Both earning and start data should be reported in studies evaluating outcome following surgery or other intervention. Considerations of age, breed, sex, track surface and gait should be included in the design of these studies.
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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 | 0.003 |
| 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) | 0.001 | 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".