2.2 Frequency, severity and circumstances of head impacts with visible signs of concussion in ice hockey
Bibliographic record
Abstract
Objective We analyzed video and helmet sensor data of head impacts in ice hockey to determine the prevalence of visible signs of concussion (VS), and test whether there were differences between head impacts with (versus without) VS in peak accelerations and rotational velocities of the head, and objects contacting the head. Design Observational cohort. Setting 51 home games across five seasons (2014–19) from a single team (BCIHL). Participants 58 men’s university ice hockey players. Outcome Measures Video footage of head impacts were analyzed to identify six types of VS (lying motionless, motor incoordination, clutching head, slow to get up, disorientation, and blank stare), and the object striking the head. For each head impact, we obtained measures of peak linear acceleration (amax) and rotational velocity (ωmax) of the head from helmet-mounted sensors (GForceTracker). Main Results VS were observed in 11% of head impacts (n=93/836). Slow to get up was observed 63 times, clutching head 40 times, motor incoordination 4 times, and disorientation once. Lying motionless and blank stare were not observed. Concussion diagnosis was unknown. amax(51 vs 38g) and ωmax(19.2 vs 14.7rad/s) were greater for head impacts with versus without VS (p≤0.003;n=534). The probability of VS was higher for stick-to-head impacts than body-to-head (OR=8.6; 95%CI=4.7–15.9) or environment-to-head impacts (13.3; 7.7–23.3), although impacting object had no effect on amax or ωmax (p≥0.574). Conclusions VS was observed in 11% of head impacts. These cases were more severe in terms of amax and ωmax, and frequently involved stick-to-head impacts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".