Concussion Incidence and Time Lost from Play in the NHL During the Past Ten Years
Bibliographic record
Abstract
BACKGROUND: The problem of concussions in professional hockey has attracted much recent attention. To evaluate the current state of this injury in the National Hockey League (NHL), we analyzed the concussion incidence and time lost from play due to concussions during the past ten NHL seasons. METHODS: Data were obtained from a complete review of injury reports in two different sports media sources covering the NHL seasons 1997-98 through 2007-08. Time lost from play was measured in missed games per concussion. RESULTS: The incidence of concussions reported in the regular season ranged from a high of 1.81/1000 athlete exposures in 1998-99 to a low of 1.04/1000 athlete exposures in 2005-06. There was a downward trend in the number of concussions reported per season during the past ten years (p=0.01). However, average time lost from play per concussion increased over the same period (p<0.0005). Forwards suffered a disproportionately high percentage of concussions (p<0.0001). CONCLUSIONS: Possibly related to injury reduction efforts, the number of concussions reported per season in the NHL has trended downward in recent years. However, the incidence of concussion remains high and the average time lost from play per concussion has increased. This may reflect increased injury severity in recent years or, alternatively, increased adherence to modern management guidelines preventing premature return to play.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".