Individual versus integration of multiple components of central blood pressure and aortic stiffness in predicting cardiovascular mortality in end-stage renal diseases
Bibliographic record
Abstract
Abstract Aortic stiffness, measured by carotid-femoral pulse-wave velocity (PWV), is a predictor of cardiovascular (CV) mortality in patients with end-stage renal disease (ESRD). Aortic stiffness increases aortic systolic and pulse pressures (cSBP, cPP) and augmentation index (AIx). In this study, we examined if the integration of multiple components of central blood pressure and aortic stiffness (ICPS) into risk score categories could improve CV mortality prediction in ESRD. In a prospective cohort of 311 patients with ESRD on dialysis who underwent vascular assessment at baseline, 118 CV deaths occurred after a medial follow-up of 3.1 years. The relationship between hemodynamic parameters and CV mortality was analyzed through Kaplan-Meier and Cox survival analysis. ICPS risk score from 0 to 5 points were calculated from points given to tertiles, and were regrouped into three risk categories (Average, High, Very High). A strong association was found between the ICPS risk categories and CV mortality (High risk HR = 2.20, 95%CI: 1.05–4.62, P = 0.036; Very High risk (HR = 4.44, 95%CI: 2.21–8.92, P < 0.001). The very high-risk category remained associated with CV mortality (HR = 3.55, 95% CI: 1.37–9.21, P = 0.009) after adjustment for traditional CV risk factors. While ICPS categories showed higher C-statistics (C: 0.627, 95%CI: 0.578–0.676, P = 0.001), it was not statistically superior to PWV, cPP or AIx. In conclusion, integration of multiple components of central blood pressure and aortic stiffness did not result in a significantly better prediction of CV mortality in this cohort.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".