A Video Analysis of Suspected Injuries and Suspected Concussions in Elite Ladies Gaelic Football Matches
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Previous research shows that injuries are prevalent in ladies Gaelic football. However, little is known about how these injuries occur (ie, the mechanism of injury). In addition, there are limited data on injuries sustained during elite-level matches. Concussions are also a key concern, yet research has examined solely self-reported suspected concussions, and it remains unclear how potential concussions are identified and managed during matches. OBJECTIVE: To establish the incidence, characteristics, and management of suspected injuries and concussions in elite ladies Gaelic football matches. DESIGN: Cross-sectional video analysis study. LEVEL OF EVIDENCE: Level 3. METHODS: A video coding framework was developed based on similar published studies and validated by 5 Gaelic football-specific raters. One research assistant coded all matches from the 2022 season, and an experienced referee also reviewed foul play events. RESULTS: There were 829 suspected injuries (suspected injury rate [IR], 229.0 per 1000 hours; 95% CI, 214.0-245.2; 6.9 suspected injuries per match) and 162 suspected concussions (IR, 44.8 per 1000 hours; 95% CI, 38.4-52.2; 1.4 per match) recorded in 120 matches. Most suspected injuries received onfield medical attention (84.0%); however, just 13.6% of suspected concussions were removed from play. The tackle accounted for the most suspected injuries (40.2%), player-to-player contact (68.2%) was the most common mechanism, and the head/neck (38.1%) was the body location injured most frequently. Foul play concerned 53.2% of suspected injuries, with 76.7% of fouls concerning the tackle. CONCLUSION: The nonremoval of suspected concussions and the frequency of tackle-related suspected injuries and foul play warrants attention. CLINICAL RELEVANCE: Developing and implementing injury prevention programs, concussion management strategies, and education for all knowledge users may contribute to a safer playing environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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 it