Pulse Wave Velocity and Prognosis in End-Stage Kidney Disease
Bibliographic record
Abstract
High pulse wave velocity (PWV) is a hallmark of end-stage kidney disease (ESKD) where it is considered useful for risk stratification. We investigated whether PWV adds meaningful prognostic information to 2 simple, well-validated, clinical risk scores specific to ESKD (the Annualized Rate of Occurrence scores) for predicting all-cause and cardiovascular mortality by applying state-of-the-art prognostic tests including discrimination (Harrell C-index), risk reclassification (integrated discrimination improvement), and calibration. We performed these analyses in the 2 largest ESKD cohorts with available PWV data, the Manhes-Hospital cohort in Paris (n=287 patients) and the Quebec Research Center cohort in Canada (n=246 patients). The Harrell C-index of the 2 clinical risk scores was consistently higher than that by PWV both for all-cause (Manhes cohort, 77.5% versus 73.7%; Quebec cohort, 61.5% versus 58.9%) and cardiovascular mortality (Manhes cohort, 77.9% versus 77.2%; Quebec cohort, 63.8% versus 60.3%). Furthermore, PWV provided a very modest increase in discriminatory power over and above clinical risk scores by Harrell C-index (from 0.5% to 1.8%) and in risk reclassification by Integrated Discrimination Improvement (from 0.9% to 5.1%) and actually worsened models calibration. In patients with ESKD, PWV has a prognostic power for all-cause and cardiovascular mortality inferior to that by simple clinical risk scores and only modestly improves the risk discrimination and reclassification by the same risk scores and worsens models calibration. Clinicians may better rely on available clinical risk scores rather than on PWV for risk stratification in the ESKD population.
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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) | 0.000 | 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".