Rodeo Catastrophic Injuries and Registry: Initial Retrospective and Prospective Report
Bibliographic record
Abstract
OBJECTIVE: To introduce the Rodeo Catastrophic Injury Registry (RCIR) and quantify the nature and incidence of catastrophic injury and fatality in rodeo participants across North America. DESIGN: Retrospective and prospective collection of catastrophic and fatal injury data in rodeo using an online registry (RCIR). SETTING: Canada and the United States. PARTICIPANTS: North American rodeo competitors. ASSESSMENT OF RISK FACTORS: Age, gender, level of competition, rodeo event, mechanism of injury, and use of protective equipment. MAIN OUTCOME MEASURES: Frequency, incidence, and nature of catastrophic injuries and fatalities among rodeo participants. RESULTS: The incidence rate of catastrophic injury from 1989 to 2009 was 9.45 per 100 000 (49/518 286). The incidence rate of catastrophic injury during the 2007-2009 study period was 19.81 per 100 000 (19/95 892). The incidence rate of fatality from 1989 to 2009 was 4.05 per 100 000 (21/518 286). The incidence rate of fatality for the 2007-2009 study period was 7.29 per 100 000 (7/95 892). CONCLUSIONS: Thoracic compression mechanisms of injury are most pervasive and likely to be fatal in rodeo and bull riding. It is unknown whether rodeo protective vests have a protective effect in reducing catastrophic and fatal injuries. On the contrary, helmet use in bull riding and rodeo events seems to have a protective effect in reducing both catastrophic injury and fatality.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".