2.5 AORTIC-BRACHIAL STIFFNESS MISMATCH AND MORTALITY IN DIALYSIS PATIENTS
Bibliographic record
Abstract
We have shown that regression of brachial stiffness is inversely related to aortic stiffness in dialysis patients. In this study, we sequentially examine the impact of aortic stiffness, brachial stiffness and aortic-brachial stiffness mismatch on mortality in dialysis patients. This is a prospective longitudinal study conducted in 310 adult dialysis patients (mean age 65 ± 15). Aortic and brachial stiffness were respectively measured by determination of carotid-femoral (cf-PWV) and carotid-radial pulse wave velocity (cr-PWV) (CompliorSP-direct measurement technique). Aortic-brachial stiffness mismatch was defined by cf-PWV/cr-PWV mismatch. Central pulse wave profile was determined by radial applanation tonometry. After a mean follow-up of 3.6 ±1.7 years mortality status was assessed. ROC curve analysis was performed to evaluate the impact of central pulse pressure (PP), heart rate adjusted augmentation index (AIx), cf-PWV, cr-PWV and the cf-PWV/cr-PWV ratio on mortality. The cf-PWV was 13.5 ± 4.1 m/s, cr-PWV was 8.7 ±1.7 m/s, cf-PWV/cr-PWV ratio was 1.6 ± 0.5, central PP was 49 ± 21 mmHg and the AIx 26.8 ± 11.1%. During follow-up, 160 (49%) deaths occurred. Area under the curve was largest for cf-PWV/cr-PWV ratio (0.694, p < 0.001), followed by cf-PWV (0.627, p < 0.001), AIx (0.617, p < 0.001), PP (0.598 , P = 0.003) and cr-PWV (0.371, p < 0.001). Figure 1 shows patient survival according to tertiles of aortic-brachial stiffness ratio. In univariate and various adjusted models using Cox regression model, aortic-brachial stiffness was independently associated with increased risk of mortality. Aortic-brachial stiffness mismatch was better that aortic stiffness alone in predicting clinical outcome in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".