The Prognostic Importance of Impaired Fasting Glycemia in Chronic Coronary Heart Disease Patients
Bibliographic record
Abstract
OBJECTIVES: Impaired glucose metabolism represents one the most important cardiovascular risk factors, with steeply raising prevalence in overall population. We aimed to compare mortality risk of impaired fasting glycaemia (IFG) and overt diabetes mellitus (DM) in patients with coronary heart disease (CHD). STUDY DESIGN: prospective cohort study METHODS: A total of 1685 patients, 6-24 months after myocardial infarction and/or coronary revascularization at baseline, were followed in a prospective cohort study. Overt DM was defined as fasting glucose ≥ 7 mmol/L and/or use of antidiabetic treatment, while IFG as fasting glucose 5.6-6.99 mmol/L, but no antidiabetic medication. The main outcomes were total and cardiovascular mortality during 5 years of follow-up. RESULTS: During follow-up of 1826 days, 172 patients (10.2%) deceased, and of them 122 (7.2%) from a cardiovascular cause. Both exposures, overt DM (n=623, 37.0% of the whole sample) and IFG (n=436, 25.9%) were associated with an independent increase of 5-year total mortality, compared to normoglycemic subjects [fully adjusted hazard risk ratio (HRR) 1.63 (95%CI: 1.01-2.61)]; p=0.043 and 2.25 (95%CI: 1.45-3.50); p<0.0001, respectively]. In contrast, comparing both glucose disorders one with each other, no significant differences were found for total mortality [HRR 0.82 (0.53-1.28); p=0.33]. Taking 5-years cardiovascular mortality as outcome, similar pattern was observed [HRR 1.96 (95%CI: 1.06-3.63) and 3.84 (95%CI: 2.19-6.73) for overt DM and IFG, respectively, with HRR 0.63 (95%CI: 0.37-1.07) for comparison of both disorders]. CONCLUSIONS: Impaired fasting glycaemia adversely increases mortality of CHD patients in the same extent as overt DM.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".