Epidemiology of injuries in hurling: a prospective study 2007–2011
Bibliographic record
Abstract
OBJECTIVES: Hurling is a stick handling game which, although native to Ireland, has international reach and presence. The aim of this study was to report incidence and type of injuries incurred by elite male hurling players over five consecutive playing seasons. DESIGN: Prospective cohort study. SETTING: Male intercounty elite sports teams participating in the National GAA Injury Database, 2007-2011. PARTICIPANTS: A total of 856 players in 25 county teams were enrolled. PRIMARY AND SECONDARY OUTCOMES: Incidence, nature and mechanism of injury were recorded by team physicians or physiotherapists to a secure online data collection portal. Time-loss injury rates per 1000 training and match play hours were calculated and injury proportions were expressed. RESULTS: In total 1030 injuries were registered, giving a rate of 1.2 injuries per player. These were sustained by 71% (n=608) of players. Injury incidence rate was 2.99 (95% CI 2.68 to 3.30) per 1000 training hours and 61.75 (56.75 to 66.75) per 1000 match hours. Direct player-to-player contact was recorded in 38.6% injuries, with sprinting (24.5%) and landing (13.7%) the next most commonly reported injury mechanisms. Median duration of time absent from training or games, where the player was able to return in the same season, was 12 days (range 2-127 days). The majority (68.3%) of injuries occurred in the lower limbs, with 18.6% in the upper limbs. The trunk and head/neck regions accounted for 8.6% and 4.1% injuries, respectively. The distribution of injury type was significantly different (p<0.001) between upper and lower extremities: fractures (upper 36.1%, lower 1.5%), muscle strain (upper 5.2%, lower 45.8%). CONCLUSIONS: These data provide stable, multiannual data on injury patterns in hurling, identifying the most common injury problems. This is the first step in applying a systematic, theory-driven injury prevention model in the sport.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".