Survey Analysis to Assess the Effectiveness of the Bull Tough Helmet in Preventing Head Injuries in Bull Riders: A Pilot Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this pilot study was to assess the effectiveness of the Bull Tough helmet (Bull Tough, Seguin, TX) in preventing head injuries to bull riders. The hypothesis was that this helmet is effective in diminishing the incidence of head injuries in bull riders. DESIGN: This study was a retrospective study. METHODS: Surveys were mailed to 320 purchasers of the Bull Tough helmet. Participants were asked to recall the numbers of rides performed in 1999 while wearing the helmet and the number of rides performed in 1999 while not wearing the helmet. In addition, they were asked to provide the number and severity of head injuries suffered in 1999 both while wearing the helmet and while not wearing the helmet. SETTING: Participants responding to the survey were bull riders from the United States and Canada. PARTICIPANTS: Eighty-one riders responded to the survey. MAIN OUTCOME MEASUREMENTS: The primary outcome measurements were planned before data collection began and included the incidence of head injuries to bull riders both while wearing the helmet and while not wearing the helmet. RESULTS: While not wearing a helmet, the incidence of head injury was 1.54% per ride (11 head injuries/713 rides). While wearing the helmet, the incidence of head injury was 0.80% per ride (28 head injuries/3,518 rides). Using the X(2) test, the p value was 0.0570. CONCLUSIONS: This study supports the hypothesis that the Bull Tough helmet diminishes the incidence of head injury in bull riders.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".