430 A novel virtual helmet fit assessment for ice hockey and ringette players amidst the COVID-19 pandemic
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 it