Dose-Response Relationship Between Alcohol Consumption and Gout Risk: Do Subtypes of Alcoholic Beverages Make a Difference?
Bibliographic record
Abstract
OBJECTIVE: Although previous studies have explored the association of drinking with gout risk, we sought to explore the dose-response relationship and the evidence between subtypes of alcoholic beverages and gout risk. METHODS: The weekly alcoholic beverage consumption of patients in the UK Biobank was collected and calculated. The Cox regression model was applied to assess the effects of drinking alcohol in general and its subtypes on gout risk by calculating the hazard ratio (HR) and 95% CIs. Additionally, the restricted cubic splines were used to estimate the dose-response relationship between alcohol consumption and gout risk. To evaluate the robustness, we performed subgroup analysis across various demographic characteristics. RESULTS: = 0.01), whereas there was no significant association in male individuals. Moreover, the dose-response relationship showed that drinking light red wine and fortified wine could reduce the gout risk, whereas beer or cider, champagne or white wine, and spirits increased the gout risk at any dose. CONCLUSION: Our study suggested a J-shaped dose-response relationship between drinking and gout risk in female individuals, but not in male individuals. For specific alcoholic beverages, light consumption of red wine and fortified wine was associated with reduced gout risk. These findings offer new insights into the roles of alcoholic beverages in gout incidence risk, although further validation 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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".