047 Obesity, Hypertension and Diuretic Use as Risk Factors for Incident Gout: A Meta-Analysis of Cohort Studies
Bibliographic record
Abstract
Background: Gout is the most common inflammatory arthritis and its prevalence and incidence have continued to increase in recent decades. As treatment remains suboptimal, identification of high-risk groups for developing gout remains important. The aim of this systematic review and meta-analysis was to examine obesity, hypertension and diuretic use as risk factors for incident gout. Methods: Three separate search strategies were developed to select articles that had investigated obesity, hypertension and/or diuretic use as a risk factor for incident gout. These searches were conducted in Medline, Embase and CINAHL from database inception to January 2015. Relevant search terms were devised from a combination of MeSH and free-text terms for gout and combined with terms for each exposure. Included articles met the following criteria: human participants; outcome was assessed in adults (≥18 years of age); prospective or retrospective cohort study; incident gout was assessed as an outcome; the exposure studied was obesity, hypertension and/or diuretic use and the study took place in primary care or was population based. Titles and abstracts were screened by a single author and full text review of remaining articles was performed by two independent assessors. Study characteristics, design, sample size and risk estimates were extracted. Methodological quality was assessed using the Newcastle–Ottawa Scale. Using a random effects model, pooled unadjusted and adjusted risk estimates were calculated for each risk factor, requiring a minimum of three articles that used the same type of risk estimate [e.g. relative risk (RR)]. Heterogeneity was assessed using the I2 statistic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.071 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".