Transient injuries are a problem in field hockey: A prospective one‐season cohort study
Bibliographic record
Abstract
Injury definitions encompass all physical complaints, medical attention, and time loss. Expressing injury burden in terms of short- and long-term impact on athlete performance may be meaningful for coaches in terms of preparation. Therefore, our aim was to describe transient (symptoms <7 days) and substantial (symptoms ≥7 days) injuries suffered in field hockey throughout the Irish Hockey League (IHL). Following ethical approval, participants were assigned unique accounts to record injuries through a monitoring software. An all physical complaints definition was adopted. Team physiotherapists were contacted weekly to further capture and corroborate details on injuries. Transient and substantial injury classifications were applied. A total of 173 injuries in 14 690 exposure hours (11.8/1000 h) occurred. Incidence of medical attention (n = 119) and time-loss (n = 70) injuries was 8.1/1000 h and 4.8/1000 h. Teams suffered transient injuries every 2.3 weeks (median = 2, IQR = 4) at a rate of 6.3/1000 h and substantial injuries every 2.5 weeks (median = 1.5, IQR = 3.25) at a rate of 5.5/1000 h. The lower back and knee were common transient injuries (7.5%), with the hamstring a common substantial injury (11.6%). Although many field hockey injuries are substantial, transient injuries are equally as frequent. These additional means of classifying injuries inform teams of the injuries that will cause disruption within their squad.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".