Blood Calcification Propensity, Cardiovascular Events, and Survival in Patients Receiving Hemodialysis in the EVOLVE Trial
Bibliographic record
Abstract
Background and objectives Patients receiving hemodialysis are at risk of cardiovascular events. A novel blood test ( T 50 test) determines the individual calcification propensity of blood. Design, setting, participants, & measurements T 50 was determined in 2785 baseline serum samples of patients receiving hemodialysis enrolled in the Evaluation of Cinacalcet Therapy to Lower Cardiovascular Events (EVOLVE) trial and the T 50 results were related to patient outcomes. Results Serum albumin, bicarbonate, HDL cholesterol, and creatinine were the main factors positively/directly and phosphate was the main factor negatively/inversely associated with T 50 . The primary composite end point (all-cause mortality, myocardial infarction [MI], hospitalization for unstable angina, heart failure, or peripheral vascular event [PVE]) was reached in 1350 patients after a median follow-up time of 619 days. After adjustments for confounding, a lower T 50 was independently associated with a higher risk of the primary composite end point as a continuous measure (hazard ratio [HR] per 1 SD lower T 50 , 1.15; 95% confidence interval [95% CI], 1.08 to 1.22; P <0.001). Furthermore, lower T 50 was associated with a higher risk in all-cause mortality (HR per 1 SD lower T 50 , 1.10; 95% CI, 1.02 to 1.17; P =0.001), MI (HR per 1 SD lower T 50 , 1.38; 95% CI, 1.19 to 1.60; P <0.001), and PVE (HR per 1 SD lower T 50 , 1.22; 95% CI, 1.05 to 1.42; P =0.01). T 50 improved risk prediction (integrated discrimination improvement and net reclassification improvement, P <0.001 and P =0.001) of the primary composite end point. Conclusions Blood calcification propensity was independently associated with the primary composite end point, all-cause mortality, MI, and PVE in the EVOLVE study and improved risk prediction. Prospective trials should clarify whether T 50 -guided therapies improve outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".