Diabetes and mortality risk in patients undergoing coronary angiography: The KARDIO study
Bibliographic record
Abstract
BACKGROUND: Diabetes is an established risk factor for adverse cardiovascular outcomes including mortality, but the relationship between diabetes and mortality risk in the presence of the extensive or diffuse form of coronary artery disease (CAD) is controversial. AIMS: We evaluated the association between diabetes and mortality risk in patients who underwent coronary angiography using a real-life clinical database. METHODS: We utilized the KARDIO registry, which comprised data on demographics, prevalent diseases, including diabetes status, cardiovascular risk factors, coronary angiographies, and other interventions in 79,738 patients. Hazard ratios (HRs) (95% confidence intervals [CIs]) for the association between prevalent diabetes and all-cause mortality were estimated. RESULTS: During a median follow-up of 5.5 years, 11,896 all-cause deaths occurred. In analyses adjusted for age, smoking status, hypertension, family history of CAD, dyslipidaemia, urgency of intervention, body mass index, sex, and sex-age interaction, the HR (95% CI) for mortality comparing diabetes with no diabetes was 1.44 (1.38, 1.50). Following additional adjustment for the degree of CAD (1-3 vessels disease) as confirmed by angiography, the HR (95% CI) for mortality remained similar 1.43 (1.36, 1.49). The association did not vary significantly across several relevant clinical characteristics except for a stronger association in those with a family history of CAD than those without (p = 0.034) and former smokers than nonsmokers (p = 0.046). CONCLUSION: In patients undergoing coronary angiography, diabetes is associated with an increased mortality risk, independent of several risk factors including the degree of CAD. The association may be modified by family history of CAD and smoking status.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".