Risk of Cancer in Middle-aged Patients With Gout: A Nationwide Population-based Study in Korea
Bibliographic record
Abstract
OBJECTIVE: Gout is reportedly associated with a higher incidence of cancer. However, patients with gout tend to have several cancer-related factors including obesity, smoking, and alcohol consumption; thus, the precise association between gout and cancer risk remains unclear. We aimed to investigate the risk of cancer in Korean patients with gout. METHODS: Based on the Korea Health Insurance Service database, the subjects comprised patients aged 41-55 years with gout newly diagnosed between 2003 and 2007. We used a multivariable-adjusted Cox proportional hazards model in gout patients and a 1:2 ratio for the matched controls by age, sex, and index year. RESULTS: We compared 4176 patients with gout with 8352 controls. The mean age and follow-up duration were 48.8 years and 10.1 years in both groups. Overall cancer risk was significantly different between gout patients and controls (HR 1.224, 95% CI 1.073-1.398). The all-cause mortality (HR 1.457, 95% CI 1.149-1.847) and cancer mortality (HR 1.470, 95% CI 1.020-2.136) were higher in patients with gout. In the subgroup analysis, the cancer risks of the stomach (HR 1.710, 95% CI 1.221-2.395), head and neck (HR 1.850, 95% CI 1.071-3.196), and hematologic or lymphoid organ (HR 2.849, 95% CI 1.035-7.844) were higher in patients with gout. CONCLUSION: Patients aged 41-55 years with gout have a higher risk of cancer and all-cause and cancer mortality compared with the general population. Therefore, special attention should be paid to higher cancer risk and mortality in these patients who are diagnosed in middle age.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".