Trends of venous thromboembolism risk before and after diagnosis of gout: a general population-based study
Bibliographic record
Abstract
OBJECTIVE: To estimate the overall risk and the temporal trend of venous thromboembolism (VTE), deep vein thrombosis (DVT), and pulmonary embolism (PE) before and after gout diagnosis in an incident gout cohort compared with the general population. METHODS: We conducted a matched cohort study using a province-wide population-based administrative health database in Canada. We calculated incidence rates (IRs) and multivariable adjusted hazard ratios (HRs) for the risk of VTE, DVT and PE before and after gout diagnosis. RESULTS: Among 130 708 incident individuals with gout (64% male, mean age 59 years), 2071 developed VTE, 1377 developed DVT and 1012 developed PE. IRs per 1000 person-years for gout were 2.63, 1.74 and 1.28 compared with 2.03, 1.28 and 1.06 for non-gout, respectively. The fully adjusted HRs (95% CI) for VTE, DVT and PE were 1.22 (1.13, 1.32), 1.28 (1.17, 1.41) and 1.16 (1.05, 1.29). For the pre-gout period, the fully adjusted HRs (95% CI) were 1.51 (1.38, 1.64), 1.55 (1.40, 1.72) and 1.47 (1.31, 1.66) for VTE, DVT and PE. During the third, second and first years preceding gout, the fully adjusted HRs for VTE were 1.44, 1.56 and 1.62. During the first, second, third, fourth and fifth years after gout, the fully adjusted HRs were 1.63, 1.29, 1.33, 1.28 and 1.22. Similar trends were also seen for DVT and PE. CONCLUSION: Increased risks of VTE, DVT and PE were found both before and after gout diagnosis. The risk increased gradually before gout, peaking in the year prior to diagnosis, and then progressively declined. Gout-associated inflammation may contribute to venous thrombosis risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".