Head injuries and Parkinson's disease in a case-control study
Bibliographic record
Abstract
BACKGROUND: Head injury is a hypothesised risk factor for Parkinson's disease, but there is a knowledge gap concerning the potential effect of injury circumstances (eg, work-related injuries) on risk. The objective of this study is to address this gap while addressing issues of recall bias and potential for reverse causation by prediagnosis symptoms. METHODS: We conducted a population based case-control study of Parkinson's disease in British Columbia, Canada (403 cases, 405 controls). Interviews queried injury history; whether injuries occurred at work, in a motor vehicle accident or during sports. Participants were also asked to report their suspicions about the causes of Parkinson's disease to provide an indicator of potential recall bias. Associations were estimated with logistic regression, adjusted for age, sex and smoking history. RESULTS: Associations were strongest for injuries involving concussion (OR: 2.08, 95% CI 1.30 to 3.33) and unconsciousness (OR: 2.64, 95% CI 1.39 to 5.03). Effects remained for injuries that occurred long before diagnosis and after adjustment for suspicion of head injury as a cause of Parkinson's disease. Injuries that occurred at work were consistently associated with stronger ORs, although small numbers meant that estimates were not statistically significant. CONCLUSIONS: This study adds to the body of literature suggesting a link between head injury and Parkinson's disease and indicates further scrutiny of workplace incurred head injuries is warranted.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".