Risk of head injury associated with distinct head impact events in elite women's hockey
Bibliographic record
Abstract
Head injuries are a major health concern for sport participants as 90% of emergency department visits for sport-related brain injuries are concussion related. 1 Recently, reports have shown a higher incidence of sport-related concussion in female athletes compared to males. 3 Few studies have described the events by which concussions occur in women's hockey, 2,7,8 however a biomechanical analysis of the risk of concussion has not yet been conducted. Therefore, the purpose of this study was to identify the highest risk concussive events in elite women's hockey and characterize these events through reconstructions to identify the associated levels of peak linear and angular acceleration and strain from finite element analysis. 44 head impact events were gathered from elite women's hockey game video and analyzed for impact event, location and velocity. In total, 27 distinct events based on impact event, location and velocity were reconstructed using a hybrid III headform and various testing setups to obtain dynamic response and brain tissue response. A three-way Multivariate Analysis of Variance (MANOVA) was conducted to determine the influence of event, location and velocity. The results of this study show that head- to-ice impacts resulted in significantly higher responses compared to shoulder-to- head collisions and head-to boards impacts however, shoulder and boards impacts were more frequent. All events produced responses comparable to proposed concussion threshold values. 21 This research demonstrates the importance of considering the event, the impact characteristics, the magnitude of response, and the frequency of these impacts when attempting to capture the short and long term risks of brain trauma in women's hockey.
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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 | 0.001 | 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) | 0.000 | 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 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".