Alcohol use disorders and risk of Parkinson’s disease: findings from a Swedish national cohort study 1972–2008
Bibliographic record
Abstract
BACKGROUND: Alcohol has been suggested to be either protective of, or not associated with Parkinson's disease (PD). However, experimental animal studies indicate that chronic heavy alcohol consumption may have dopamine neurotoxic effects relevant for PD. We studied the association between diagnosed alcohol use disorders and PD. METHODS: All individuals in Sweden admitted with a diagnosis of an alcohol use disorder or appendicitis (reference group) between January 1, 1972 and December 31, 2008 were identified through the Swedish National Inpatient Register, and followed for up to 37 years for a diagnosis of PD. We estimated hazard ratios (HR) with 95% confidence intervals (CI) and adjusted for age and sex. RESULTS: We found 1,741 (0.3%) cases of PD in the cohort of 602,930 individuals, 1,083 (0.4%) among those admitted with an alcohol use disorder and 658 (0.2%) of the individuals admitted with appendicitis. The mean follow-up time was 13.6 and 17.1 years, respectively. The HR for PD associated with an alcohol use disorder was 1.38 (CI 1.25-1.53) adjusted for age and sex. When the risk was estimated in age groups for first hospital admission with PD the highest risk was observed in the lowest age group, ≤44, HR 2.39 (0.96-5.93), adjusted for age at exposure and sex. CONCLUSIONS: A history of an alcohol use disorder conferred an increased risk of admission with a diagnosis of Parkinson's disease in both women and men. In particular, the risk seemed higher at lower ages of first admission with Parkinson's disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".