Risk of fragility fracture among patients with gout and the effect of urate-lowering therapy
Bibliographic record
Abstract
BACKGROUND: Previous studies that quantified the risk of fracture among patients with gout and assessed the potential effect of urate-lowering therapy have provided conflicting results. Our study aims to provide better estimates of risk by minimizing the effect of selection bias and confounding on the observed association. METHODS: We used data from the Clinical Practice Research Datalink, which records primary care consultations of patients from across the United Kingdom. We identified patients with incident gout from 1990 to 2004 and followed them up until 2015. Each patient with gout was individually matched to 4 controls on age, sex and general practice. We calculated absolute rate of fracture and hazard ratios (HRs) using Cox regression models. Among patients with gout, we assessed the impact of urate-lowering therapy on fracture, and used landmark analysis and propensity score matching to account for immortal time bias and confounding by indication. RESULTS: We identified 31 781 patients with incident gout matched to 122 961 controls. The absolute rate of fracture was similar in both cases and controls (absolute rate = 53 and 55 per 10 000 person-years, respectively) corresponding to an HR of 0.97 (95% confidence interval 0.92-1.02). Our finding remained unchanged when we stratified our analysis by age and sex. We did not observe statistically significant differences in the risk of fracture among those prescribed urate-lowering therapy within 1 and 3 years after gout diagnosis. INTERPRETATION: Overall, gout was not associated with an increased risk of fracture. Urate-lowering drugs prescribed early during the course of disease had neither adverse nor beneficial effect on the long-term risk of fracture.
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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 | 0.002 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".