430 A novel virtual helmet fit assessment for ice hockey and ringette players amidst the COVID-19 pandemic
Bibliographic record
Abstract
<h3>Background</h3> Proper helmet fit is an important consideration for preventing head injuries, including concussions, in helmeted sports like youth ice hockey and ringette. Helmet fit assessments are typically completed in-person; however, this was not possible given COVID-19 restrictions. Thus, alternative considerations for virtual assessments were required. <h3>Objective</h3> To examine the feasibility and inter-rater reliability of virtual ice hockey and ringette helmet fit assessments. <h3>Design</h3> Cross-sectional. <h3>Setting</h3> Calgary, Canada. <h3>Participants</h3> Elite/upper division youth (ages 13–18) ice hockey (n=31 males) and ringette (n=30 females) players. <h3>Assessment of Risk Factors</h3> Standardized ice hockey/ringette helmet fit criteria were developed and reliable for in-person assessments. Criteria were adapted for virtual delivery to participants over ZOOM video platform individually by two trained assessors per sport. <h3>Main Outcome Measurements</h3> Twelve helmet fit criteria scored as yes/proper fit or no/poor fit were used to assess helmet shell fit (e.g., helmet fits snug, doesn’t cover eyes), positioning (e.g., helmet is 1–2 finger widths above eyebrows, covers base of skull), facemask fit (e.g., chin piece fits, facemask does not move left/right), and others. Percent agreement (PA) between raters was used to describe inter-rater reliability, and each rater documented barriers for completing the assessments virtually. <h3>Results</h3> Acceptable PA (>80%) was demonstrated for 8/12 criterion for ice hockey and 9/12 for ringette. Below acceptable agreement was found for all four criterion assessing the helmet facemask fit (PA range: 48%-74%) in ice hockey players and criteria for the chin straps fit (PA=66%), helmet positioning (PA=73%), and facemask fit (PA=63%) in ringette players. Common barriers were related to technology (e.g., audio/video quality) and environment (e.g., noisy, lighting). <h3>Conclusions</h3> Virtual helmet fit assessments are feasible and reliable for most criteria, with more training required for criteria below acceptable agreement. Virtual assessments provides another option for assessing helmet fit for concussion prevention in helmeted sports.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".