4820Assessing the predictive value of coronary artery calcium score for predicting all-cause mortality in patients with renal impairment
Bibliographic record
Abstract
Background: Renal impairment is considered as a coronary artery disease (CAD) equivalent. Yet, the evidence for an independent association of coronary artery calcium score (CACS) with adverse cardiovascular outcomes in patients with impaired renal function remains unclear. Purpose: The current study therefore sought to assess whether CACS improves risk stratification as well as augments prediction of adverse outcomes beyond risk prediction algorithm in asymptomatic patients with renal impairment. Methods: We identified 45,174 asymptomatic Korean adults (mean age: 52.1±9.6 years, 70.6% male) without known CAD who underwent CAC screening, and with renal impairment [estimated glomerular filtration rate (eGFR) 30–89 ml/min/1.73 m2 by the MDRD equation]. The eGFR was categorized as 60–89 (n=41,425) and 30–59 ml/min/1.73m2 (n=3,749). CACS was categorized as follows: 0, 1–100, 101–400, and >400. All-cause mortality incidence per 1,000 person-years and multivariable Cox proportional hazards models with 95% confidence interval (95% CI) were utilized. Discrimination by C-statistic and category-free net reclassification improvement (cNRI) were estimated for all-cause 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 | 0.002 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".