Fibroblast growth factor 23 as a risk factor for cardiovascular events and mortality in patients in the <scp>EVOLVE</scp> trial
Bibliographic record
Abstract
INTRODUCTION: High mortality rates in patients with chronic kidney disease-mineral and bone disorder (CKD-MBD) receiving maintenance hemodialysis are largely due to cardiovascular (CV) events. METHODS: We evaluated associations between MBD parameters, fibroblast growth factor 23 (FGF23) concentrations, and clinically adjudicated CV events from the Evaluation of Cinacalcet Hydrochloride Therapy to Lower Cardiovascular Events (EVOLVE) trial. Patients enrolled in EVOLVE, who had not experienced any study endpoints between randomization and week 20 with evaluable baseline and week 20 values for key laboratory parameters (parathyroid hormone, calcium, phosphate, and FGF23), were assessed. We used adjusted Cox proportional hazards regression models to estimate relative risk of outcomes (primary composite, all-cause mortality, and CV events) based on FGF23 and MBD parameters. Laboratory values were modeled with linear terms and using natural cubic splines with two degrees of freedom. FINDINGS: For the primary endpoint, patients assessed (N = 2309) were followed up over a mean duration of 3.1 years, during which 1037 CV events (497 deaths, 540 nonfatal events) occurred. Adjusted models showed an association between FGF23 and the risk of CV events. Hazard ratio per log unit of FGF23 at week 20 was 1.09 [95% CI: 1.03-1.16], and the hazard ratio per log unit change in FGF23 from week 0 to week 20 was 1.09 [95% CI: 1.00-1.17]. DISCUSSION: Our data highlight FGF23 as an independent CV risk factor and potential biomarker and therapeutic target for patients with CKD-MBD receiving maintenance hemodialysis.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".