Associations between concussion and risk of diagnosis of psychological and neurological disorders: a retrospective population-based cohort study
Bibliographic record
Abstract
OBJECTIVE: To investigate associations between concussion and the risk of follow-up diagnoses of attention-deficit hyperactivity disorder (ADHD), mood and anxiety disorders (MADs), dementia and Parkinson's disease. DESIGN: A retrospective population-based cohort study. SETTING: Administrative health data for the Province of Manitoba between 1990-1991 and 2014-2015. PARTICIPANTS: A total of 47 483 individuals were diagnosed with a concussion using International Classification of Diseases (ICD) codes (ICD-9-CM: 850; ICD-10-CA: S06.0). All concussed subjects were matched with healthy controls at a 3:1 ratio based on age, sex and geographical location. Associations between concussion and conditions of interest diagnosed later in life were assessed using a stratified Cox proportional hazards regression model, with adjustments for socioeconomic status and pre-existing medical conditions. RESULTS: 28 021 men (mean age ±SD, 25±18 years) and 19 462 women (30±21 years) were included in the concussion group, while 81 871 men (25±18 years) and 57 159 women (30±21 years) were included in the matched control group. Concussion was associated with adjusted hazard ratios of 1.39 (95% CI 1.32 to 1.46, p<0.001) for ADHD, 1.72 (95% CI 1.69 to 1.76; p<0.001) for MADs, 1.72 (95% CI 1.61 to 1.84; p<0.001) for dementia and 1.57 (95% CI 1.41 to 1.75; p<0.001) for Parkinson's disease. CONCLUSION: Concussion was associated with an increased risk of diagnosis for all four conditions of interest later in life.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".