Predictors of Concussion Outcomes in Individuals With ADHD
Bibliographic record
Abstract
OBJECTIVES: To describe and delineate the epidemiological profile of concussion injuries in individuals with attention-deficit/hyperactivity disorder (ADHD) by identifying characteristics associated with poorer outcomes. SETTING: One hundred forty-four multidisciplinary concussion-specialized clinics across Canada. PARTICIPANTS: Two hundred twenty-two individuals with a diagnosis of ADHD aged 7 to 53 years who sustained a concussion within the last year. DESIGN: Multicenter cohort study. MAIN MEASURES: Candidate predictor variables (ie, age, sex, concussion history, loss of consciousness, and internalized and learning disorder comorbidities) were collected through oral interviews. Concussion outcomes (ie, symptom severity and total number of symptoms experienced) were assessed with the Sport Concussion Assessment Tool. RESULTS: Older age, female sex, and the presence of an internalized disorder predicted poorer concussion outcomes in individuals with ADHD. Males with ADHD reported significantly worse concussion outcomes with increasing age, while outcomes remained fairly stable across age in females. CONCLUSION: The current findings represent a promising step toward the optimization of concussion management in individuals with ADHD. With a more thorough understanding of the demographic and comorbidity variables, clinical care decisions and intervention strategies can be developed to help individuals with ADHD who might be at a higher risk of poorer outcomes following a concussion.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".