Contribution of impaired renal function to cardiovascular risk prediction models in renal transplant recipients
Bibliographic record
Abstract
BACKGROUND: The Framingham risk score (FRS) and cardiovascular risk calculator for renal transplant recipients (CRCRTR-MACE) quantify cardiovascular risk in renal transplant recipients (RTR). In contrast to the FRS, the CRCRTR-MACE includes serum creatinine as a variable in the risk prediction equation. OBJECTIVE: To determine the influence of impaired renal function on performances of the two equations. METHODS: A chart review of 270 RTR transplanted from 1979 to 2012. High risk was defined at scores ≥20%. Standard statistical analyses included multivariate analysis (MVA), stepwise analysis, and odds ratio to estimate contributions of risk factors. RESULTS: Mean transplant duration was 9.51 ± 6.65 yr. Mean eGFR was 59.19 ± 28.26 mL/min/1.73 m(2) . FRS and CRCRTR-MACE scores of least 20% were present in 9.3% and 24.8%, respectively, while 7.2% and 11.2% of RTR with eGFR ≥60 mL/min/1.73 m(2) were high risk, respectively. Mean age, blood pressure, TC:HDL ratio, smoking, and diabetes were evenly distributed in patients with varying eGFR. FRS scores remained similar at wide eGFR range (≤30 mL/min/1.73 m(2) -≥90 mL/min/1.73 m(2) ), while CRCRTR-MACE scores significantly increased as eGFR decreased. CONCLUSIONS: CRCRTR-MACE identified more patients at high cardiovascular risk, even in those with more favorable renal function, suggesting a fundamental difference between the two calculators beyond renal function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".