Additional file 1: of Gender-specific risk factors for gout: a systematic review of cohort studies
Bibliographic record
Abstract
Table S1. The results of the quality appraisal of the included articles using the Newcastle-Ottawa Scale (NOS). Table S2. Risk estimates for developing gout based on age. Table S3. Risk estimates for developing gout based on ethnicity. Table S4. Risk estimates for developing gout based on diet. Table S5. Risk estimates for developing gout based on caffeine consumption. Table S6. Risk estimates for developing gout based on fructose consumption. Table S7. Risk estimates for developing gout based on vitamin C consumption. Table S8. Risk estimates for developing gout based on alcohol consumption. Table S9. Risk estimates for developing gout based on metabolic syndrome. Table S10. Risk estimates for developing gout based on body mass index (BMI). Table S11. Risk estimates for developing gout based on waist and chest circumference. Table S12. Risk estimates for developing gout based on waist-to-hip ratio. Table S13. Risk estimates for developing gout based on weight change. Table S14. Risk estimates for developing gout based on diabetes mellitus. Table S15. Risk estimates for developing gout based on dyslipidaemias. Table S16. Risk estimates for developing gout based on renal disease. Table S17. Risk estimates for developing gout based on hypertension. Table S18. Risk estimates for developing gout based on diuretic use. Table S19. Risk estimates for developing gout based on psoriasis and psoriatic arthritis (PsA). Table S20. Risk estimates for developing gout based on anti-diabetic medication. (DOCX 149 kb)
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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 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.638 | 0.022 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".