11.13 Examining game-related factors associated with concussions displaying no visible signs
Bibliographic record
Abstract
Objective To examine game-related factors that are most reliably associated with concussions in which players do not demonstrate on-ice visible signs. Design Retrospective cohort. Setting National Hockey League (NHL) Regular Season Games over two seasons (2014–15 & 2015–16). Participants NHL players diagnosed with concussion; analysis was limited to events where digital video of the injury was available (n=151). Interventions (or Assessment of Risk Factors) Game-related factors: ice location, time zone change from prior game, time of season, hits received and given within the same game and the last 7 days, and whether the injured player played the previous day. Outcome Measures Both univariate and multivariate analyses were completed to examine differences in game-related factors between concussed players with no visible signs (-VS) and those with visible signs (+VS). Main Results Univariate analysis between the -VS and +VS groups found no statistically significant differences. PLS discriminant analysis found that concussions with -VS were associated with fewer hits given in the last 7 days (bootstrap ratio = 2.3, p = 0.02), fewer hits given in the last game (bootstrap ratio = 2.3, p = 0.02), and fewer total hits in the last game (bootstrap ratio = 2.1, p = 0.03). Conclusions The present study found that players who sustained a concussion with no visible signs had fewer hits given and fewer total hits in the proceeding game(s).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".