Colchicine Use Is Associated with Decreased Prevalence of Myocardial Infarction in Patients with Gout
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The ability of antiinflammatory strategies to alter cardiovascular risk has not been rigorously examined. Colchicine is an antiinflammatory agent that affects macrophages, neutrophils, and endothelial cells, all of which are implicated in the pathogenesis of cardiovascular disease. We examined whether colchicine use was associated with a reduced risk of myocardial infarction (MI) in patients with gout. METHODS: We conducted a retrospective, cross-sectional study of all patients with an International Classification of Diseases, 9th ed, code for gout in the electronic medical record (EMR) of the New York Harbor Healthcare System Veterans Affairs network and ≥ 1 hospital visit between August 2007 and August 2008. Hospital pharmacy data were used to identify patients who had filled at least 1 colchicine prescription versus those who had not. Demographics and CV comorbidities were collected by EMR review. The primary outcome was diagnosis of MI. Secondary outcomes included all-cause mortality and C-reactive protein (CRP) level. RESULTS: In total, 1288 gout patients were identified. Colchicine (n = 576) and no colchicine (n = 712) groups had similar baseline demographics and serum urate levels. Prevalence of MI was 1.2% in the colchicine versus 2.6% in the no-colchicine group (p = 0.03). Colchicine users also had fewer deaths and lower CRP levels, although these did not achieve statistical significance. Colchicine effects persisted when allopurinol users were excluded from the analysis. CONCLUSION: In this hypothesis-generating study, gout patients who took colchicine had a significantly lower prevalence of MI and exhibited trends toward reduced all-cause mortality and lower CRP level versus those who did not take colchicine.
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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 | 0.001 | 0.001 |
| 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 it