Association between Serum Interleukin-6 Concentration and Mortality in Patients with Coronary Artery Disease
Bibliographic record
Abstract
OBJECTIVES: To evaluate whether serum interleukin-6 (IL-6) is associated with increased risk of mortality in coronary artery disease (CAD) patients. METHODS: We performed a prospective cohort study of 718 CAD patients from the Guangzhou Cardiovascular Disease Cohort (GCDC) study. Multivariable-adjusted Cox proportional hazards regression analyses were used to examine the association between serum IL-6 with all-cause and cardiovascular mortality. RESULTS: During the 1663 person-years of followup, the cumulative all-cause mortality and cardiovascular mortality were 6.5% (n = 47) and 3.3% (n = 24), respectively. The mean length of followup was 2.32 ± 0.81 years. In the multivariable analyses, a one-SD increment in log-transformed serum IL-6 was positively associated with an increased risk of all-cause and cardiovascular mortality, with hazard ratios (HR) of 2.93 (95% CI, 2.11-4.08) and 2.04 (95% CI, 1.34-3.68) within the patients combined and 2.98 (95% CI, 2.12-4.18) and 3.10 (95% CI, 1.98-4.85) within males, respectively. Patients in the highest serum IL-6 tertile versus the lowest tertile were at higher risk of all-cause and cardiovascular mortality, with HR of 17.12 (95% CI 3.11-71.76) and 8.68 (95% CI, 1.88-37.51), respectively. CONCLUSIONS: In hospitalized patients with CAD, serum IL-6 is significantly associated with all-cause and cardiovascular mortality.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".