Drivers of mortality in patients with chronic coronary disease in the low-dose colchicine 2 trial
Bibliographic record
Abstract
BACKGROUND: Low-dose colchicine significantly reduces the risk of cardiovascular events in patients with chronic coronary disease. An increase of non-cardiovascular death raised concerns about its safety. This study reports cause-specific mortality and baseline predictors of mortality in the Low-Dose Colchicine 2 (LoDoCo2) trial. METHODS: Patients with chronic coronary disease were randomly allocated to colchicine 0.5 mg once daily or placebo on a background of optimal medical therapy. Cause-specific mortality data were analysed, stratified by treatment status. Multivariate analyses were performed to examine the predictors of mortality as well as cardiovascular and non-cardiovascular death. RESULTS: After a median 28.6 months follow-up, 133 out of 5522 participants (2.4%) died. Forty-five deaths were cardiovascular (colchicine versus placebo: 20 [0.7%] versus 25 [0.9%], HR, 0.80; 95% CI, 0.44-1.44), while eighty-eight deaths were non-cardiovascular (53 [1.9%] versus 35 [1.3%]; HR, 1.51; 95% CI, 0.99-2.31). Forty-eight deaths were due to cancer (26 [0.9%] versus 22 [0.8%]), thirteen end-stage pulmonary disease (9 [0.3%] versus 4 [0.1%]), eight infection (4 [0.1%] versus 4 [0.1%]), five dementia (4 [0.1%] versus 1 [0.0%]) and five related multiple organ failure (3 [0.1%] versus 2 [0.1%]). Multivariable analysis demonstrated age > 65 years was the only independent baseline characteristic associated with non-cardiovascular death (HR, 3.65; 95% CI, 2.06-6.47). CONCLUSIONS: During the LoDoCo2 trial, assignment to colchicine was not associated with an adverse effect on any specific causes of death. Most deaths were related to non-cardiovascular causes, underscoring the importance of comorbidities as drivers of all-cause mortality in patients with chronic coronary disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".