Associations between concussion and risk of diagnosis of psychological and neurological disorders: a retrospective population-based cohort study
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it