Association between serum osteoprotegerin level and mortality in kidney transplant recipients ‐ a prospective observational cohort study
Bibliographic record
Abstract
Paradoxically, higher serum levels of osteoprotegerin (OPG: a vascular calcification inhibitor) have been associated with increased arterial stiffness, risk of cardiovascular disease and all-cause mortality. A few studies reported that post-transplant OPG levels are associated with mortality in kidney transplant (KT) recipients. In this study, this association was assessed in a cohort of prevalent KT recipients, adjusting for previously untested potential confounders, including fibroblast growth factor 23 (FGF23) and interleukin 6 (IL-6). Socio-demographic and clinical parameters, medical and transplant history, and laboratory data were collected from 982 prevalent KT recipients. The association between serum OPG and all-cause mortality over a 6-year follow-up period was examined using Kaplan-Meier survival curves and multivariable-adjusted Cox regression models. Participants with high serum OPG were more likely female, older, deceased donor KT recipients and have more comorbidity, lower eGFR, higher FGF23, higher IL-6, and longer dialysis vintage. Each 1 pmol/l higher serum OPG level was associated with a 49% higher risk of mortality (hazard ratio (HR) [95% confidence interval (CI)]: 1.49 [1.40-1.61]). This association persisted after adjusting for confounders (HR [95% CI]: 1.20 [1.10-1.30]). In conclusion, serum OPG was associated with all-cause mortality independent of several novel confounders in prevalent KT recipients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".