Baseline Evaluation in Youth Ice Hockey Players: Comparing Methods for Documenting Prior Concussions and Attention or Learning Disorders
Bibliographic record
Abstract
STUDY DESIGN: Cross-sectional. OBJECTIVE: To examine differences in concussion history and attention or learning disorders reported by elite youth ice hockey players, using a questionnaire that allows parental input compared to a clinic-based test battery that does not. BACKGROUND: A history of previous concussion and the presence of attention or learning disorders can affect concussion-management decisions; however, youth athletes may not accurately report their medical history because they may not know or recall important details. METHODS: The sample included 714 Bantam (ages 12-14 years) and Midget (ages 15-17 years) ice hockey players (601 male, 113 female) from the most elite divisions of play (AA and AAA). Players completed a take-home preseason questionnaire (PSQ) with the input of a parent/guardian, and also independently completed the baseline Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) at the beginning of the 2011-2012 hockey season. RESULTS: In 21.1% (95% confidence interval: 18.1%, 24.1%) of cases, there was disagreement between the PSQ and ImPACT in the number of previous concussions reported. Among those who reported an attention disorder on the PSQ, 85.7% also reported an attention disorder on the ImPACT. Only 9.5% of those who reported a learning disorder on the PSQ also reported a learning disorder on the ImPACT. CONCLUSION: In 1 of 5 players, reported concussion history differed between the PSQ and ImPACT, and there was substantial disagreement between instruments for those reporting learning disorders. The method of obtaining medical history may, therefore, affect baseline and postconcussion evaluations.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".