Bibliographic record
Abstract
BACKGROUND: Several metabolic studies and a recent double-blind, placebo-controlled, randomized trial have shown that higher vitamin C intake significantly reduces serum uric acid levels. Yet the relation with risk of gout is unknown. METHODS: We prospectively examined, from 1986 through 2006, the relation between vitamin C intake and risk of incident gout in 46 994 male participants with no history of gout at baseline. We used a supplementary questionnaire to ascertain the American College of Rheumatology criteria for gout. Vitamin C intake was assessed every 4 years through validated questionnaires. RESULTS: During the 20 years of follow-up, we documented 1317 confirmed incident cases of gout. Compared with men with vitamin C intake less than 250 mg/d, the multivariate relative risk (RR) of gout was 0.83 (95% confidence interval [CI], 0.71-0.97) for total vitamin C intake of 500 to 999 mg/d, 0.66 (0.52-0.86) for 1000 to 1499 mg/d, and 0.55 (0.38-0.80) for 1500 mg/d or greater (P < .001 for trend). The multivariate RR per 500-mg increase in total daily vitamin C intake was 0.83 (95% CI, 0.77-0.90). Compared with men who did not use supplemental vitamin C, the multivariate RR of gout was 0.66 (95% CI, 0.49-0.88) for supplemental vitamin C intake of 1000 to 1499 mg/d and 0.55 (0.36-0.86) for 1500 mg/d or greater (P < .001 for trend). CONCLUSIONS: Higher vitamin C intake is independently associated with a lower risk of gout. Supplemental vitamin C intake may be beneficial in the prevention of gout.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".