Characterizing Symptoms Before and Following Concussion in Professional Hockey
Bibliographic record
Abstract
OBJECTIVE: Examine SCAT5 baseline and acute symptom subscales in professional hockey players. DESIGN: Longitudinal case-control. SETTING: Preseason medical evaluations and suspected concussion evaluations. PARTICIPANTS: NHL/AHL players were given the NHL-modified SCAT5 before the 2018-2019 season (n = 1924). During the season, English-speaking players evaluated within 1 day of injury and diagnosed with concussion (n = 140) were compared with players evaluated for possible concussion, but not given a concussion diagnosis (n = 174). INDEPENDENT VARIABLES: Concussion diagnosis and demographic characteristics. MAIN OUTCOME MEASURES: Postconcussion Symptoms Scale (PCSS) items were subdivided into affective/emotional, cognitive, somatic/physical, sleep, and headache subscales. RESULTS: Cognitive (13%), somatic/physical (10%), sleep (26%), affective (18%), and headache (6%) symptoms were reported by players at baseline. Concussed players reported more acute symptoms than active controls on each of the subscales (all P's < 0.001). Players who underwent a suspected concussion evaluation were more likely to be diagnosed with concussion if they reported affective/emotional symptoms at baseline (P < 0.001). Postinjury cognitive (P < 0.001), somatic/physical (P < 0.001), and headache (P < 0.001) symptoms best differentiated concussed players and active controls. CONCLUSIONS: Player reports of increased cognitive difficulties, somatic/physical difficulties, and headache were most associated with concussion diagnosis at the acute evaluation. An abbreviated "acute" PCSS that includes only headache, cognitive, and/or physical symptoms may be useful for diagnostic purposes when evaluation time is limited. Approximately 2% of players reported significantly elevated affective/emotional symptoms at baseline and report of affective/emotional symptoms at baseline was associated with an increased likelihood of concussion diagnosis among players referred for a suspected concussion evaluation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".