Injury profile of elite under-21 age female field hockey players.
Bibliographic record
Abstract
AIM: The objective of this paper was to identify the rate, profile, and severity of injuries associated with participating on a provincial/state hockey (field) and compare these data, where possible, with available ice hockey data. METHODS: An injury was defined as ''any event, during team or team-related game, practice, and/or activity (on or off the playing surface), requiring any attention by the team's Therapist and/or Physician and subsequent game and/or practice time-loss''. Seventy-five players, under the age of 21 years participated in the study over a 5-year duration. All injury data were collected post-injury. Data were collected on the player position, games versus practice conditions, injury time, injury type, injury etiology, anatomical region and plane injured, injury status, and duration required to return to full activity. RESULTS: A total of 2 828 athletes exposure's and 198 injuries were recorded. The combined injury rate was 70 injuries per 1 000 player game and practice exposures with significantly higher risk of injury resulting during the second half of a game or practice. Backs experienced the highest percentage and have a higher risk of injuries. The predominant injuries sustained included muscle strains, followed by tendonitis, while the highest number of injuries resulted from no contact. The lower back and ankle/foot were the most vulnerable to injury, followed by the knee. CONCLUSION: From this study it can be concluded that hockey (field) players can experience higher injury rates than ice hockey. Also, field hockey players are at greater risk of injury depending on the playing position and are more likely to be injured during the latter duration of a game and/or practice. In identifying injury trends related to hockey, injury prevention strategies should be developed as players use limited protective equipment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".