The Ability of Men’s Lacrosse Helmets to Reduce the Dynamic Impact Response for Different Striking Techniques in Women’s Field Lacrosse
Bibliographic record
Abstract
BACKGROUND: Women's field lacrosse is described as a noncontact game relying primarily on rules to decrease the risk of head injuries. Despite not allowing head contact, however, concussions continue to be reported in women's field lacrosse. PURPOSE: To assess the ability of men's lacrosse helmets to decrease linear and angular acceleration for different striking techniques in women's field lacrosse. STUDY DESIGN: Controlled laboratory study. METHODS: A helmeted and unhelmeted Hybrid III 50th Percentile headform was attached to a Hybrid III neckform and were subjected to impacts by 8 striking techniques. Eleven athletic females completed 5 slashing techniques, while physical reconstruction equipment was used to simulate falls and shoulder and ball impacts to the head. Three trials were conducted for each condition, and peak resultant linear and angular accelerations of the headform were measured. RESULTS: Falls produced the highest linear and angular acceleration, followed by ball and high-velocity stick impacts. Low-velocity stick impacts were found to produce the lowest linear and angular accelerations. Men's lacrosse helmets significantly decreased linear and angular accelerations in all conditions, while unhelmeted impacts were associated with high accelerations. CONCLUSION: If women's field lacrosse is played within the rules, only falls were found to produce high linear and angular acceleration. However, ball and high-velocity stick impacts were found to produce high linear and angular accelerations. These linear and angular accelerations were found to be within the ranges reported for concussion. When the game is not played within the rules, men's lacrosse helmets provide an effective method of reducing linear and angular accelerations. Thus, women's field lacrosse may be able to reduce the occurrence of high linear and angular acceleration impacts by having governing bodies improving rules, implementing the use of helmets, or both. CLINICAL RELEVANCE: Identifying striking techniques that produce high linear and angular acceleration specific to women's lacrosse and measuring the capacity of a men's lacrosse helmet to reduce linear and angular acceleration.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".