Alcohol Intake and Risk of Incident Psoriatic Arthritis in Women
Bibliographic record
Abstract
OBJECTIVE: Alcohol intake has been associated with an increased risk of psoriasis. However, the association between alcohol intake and risk of psoriatic arthritis (PsA) has been unclear. We evaluated the association between alcohol intake and risk of incident PsA in a large cohort of US women. METHODS: Our present study included a total of 82,672 US women who provided repeated data on alcohol intake over the followup period (1991-2005). Self-reported PsA was validated using the Psoriatic Arthritis Screening and Evaluation (PASE) questionnaire. Cox proportional hazards models were used to estimate the age-adjusted and multivariate-adjusted HR and 95% CI for the PsA in association with alcohol intake. RESULTS: We documented 141 incident PsA cases during 14 years (1,137,763 person-yrs) of followup. Compared to non-drinkers, the multivariate HR for PsA were 0.70 (95% CI 0.48-1.01) for 0.1-14.9 g/day, 1.43 (95% CI 0.67-3.08) for 15.0-29.9 g/day, and 4.45 (95% CI 2.07-9.59) for ≥ 30.0 g/day of cumulative average alcohol intake. Risk estimates were generally consistent when using updated alcohol intake and baseline alcohol intake in 1991 as the exposures, and when the analysis was restricted to those who developed psoriasis during the followup. CONCLUSION: Excessive alcohol intake was associated with an increased risk of incident PsA in a cohort of US women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".