Coronary artery calcification in Korean patients with incident dialysis
Bibliographic record
Abstract
INTRODUCTION: Patients with chronic kidney disease have an extremely high risk of developing cardiovascular disease (CVD). In patients with end-stage renal disease (ESRD), coronary artery calcification (CAC) is associated with increased mortality from CVD. METHODS: The present study aimed to investigate the risk factors for CAC in Korean patients with incident dialysis. Data on 423 patients with ESRD who started dialysis therapy between December 2012 and March 2014 were obtained from 10 university-affiliated hospitals. CAC was identified by using noncontrast-enhanced cardiac multidetector computed tomography. The CAC score was calculated according to the Agatston score, with CAC-positive subjects defined by an Agatston score >0. FINDINGS: Patients' mean age was 55.6 ± 14.6 years, and 64.1% were men. The CAC-positive rate was 63.8% (270 of 423). Results of univariate analyses showed significant differences in age, sex, etiology of ESRD and comorbid conditions according to the CAC score. However, results of multiple regression analysis showed that only a higher age was significantly associated with the CAC score. Receiver operating characteristic curves showed that the sensitivity and specificity of L-spine radiography for diagnosing CAC were 56% and 91%, respectively, for diagnosing CAC (area under the curve, 0.735). DISCUSSION: CAC was frequent in patients with incident dialysis, and multiple regression analysis showed that only age was significantly associated with the CAC score. In addition, L-spine radiography could be a helpful modality for diagnosing CAC in patients with incident dialysis.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".