Anomalous Incidence of Fatal Musculoskeletal Injury in North American 2-Year-Old Thoroughbred Racehorses in the Year 2020
Bibliographic record
Abstract
Racehorse training and racing schedules in many parts of the United States and Canada were interrupted or otherwise reduced during the first three to six months of 2020. This was an indirect consequence of mitigations to prevent the spread of the pandemic virus COVID-19. Data from the Equine Injury Database, a census-level survey of all race starts made in the USA and Canada, were used to analyse the incidence of fatalities in 2009-2022 among three age cohorts of racehorses within each year. There was a statistically significant increase in the incidence of musculoskeletal fatalities among 2-year-old Thoroughbreds in 2020, compared to the period 2009-2019. In 2021 and 2022, the training schedules of 2-year-old horses returned to pre-2020 levels, as did the incidence of fatalities. The delayed start to training for 2-year-old horses was associated with an increase in risk of fatal musculoskeletal injury for those horses during 2020, but the risk for the same horses in 2021-when they were 3 years old-was not significantly different to the risk for 3-year-olds in any other year. The increased risk of fatal musculoskeletal injury in 2020 was only found among horses that were 2 years old in 2020-horses aged 3 years or more in 2020 were not at increased risk.
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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 | 0.000 |
| 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".