The study of head impact biomechanics in adolescent and youth minor ice hockey players
Bibliographic record
Abstract
Mild traumatic brain injuries are one of the most clinically difficult conditions to manage in sports medicine. Better understanding the biomechanics of head impacts will allow clinicians and researchers to better implement interventions designed specifically to reduce the incidence of injury. To date, few studies have looked at the biomechanics of head impacts in the young athlete. The overall objective of this dissertation was to evaluate the biomechanics of head impact severity during participation in youth ice hockey, with a specific evaluation of descriptive factors, and intrinsic and extrinsic factors related to impact biomechanics while playing hockey. We studied a two-year cohort of Bantam and Midget-aged ice hockey players, all of whom participated in all practices and games while wearing specially instrumented helmets capable of measuring head impact measures including linear acceleration, rotational acceleration, and the Head Impact Technology severity profile (HITsp). We also video-recorded every game in the first year of the study and developed an evaluation tool in order to characterize a number of aspects related to relative body positioning and overall anticipation of impending collisions. We also recorded a wide range of information including the number of shifts played, cervical muscle strength, player head and neck anthropometrics, measures of trait aggression, and general aerobic fitness. Our data support the notion that anticipating collisions may play a role in minimizing head impact severity. We also found impacts occurring in the open ice were greater than those occurring along the playing boards. Further, illegal player infractions occur at a relatively high frequency and typically result in higher measures of head impact severity than legal collisions, especially as it pertains to elbowing, head contact, and high sticking infractions to the head. Based on our data, it does not appear that those with stronger neck muscles are better able to mitigate the forces associated with head impacts. Our data suggest a continued need to educate our players with the necessary technical skills needed to heighten their awareness on the ice. Coaches and athletes should incorporate body collision exercises in practices, and spend time educating young athletes on these proper checking techniques in an attempt to minimize the risk of injury and increase the safety of ice hockey.
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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) | 0.001 | 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 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".