Comparison of different algorithms for the assessment of cardiovascular risk after kidney transplantation by the time of entering waiting list
Bibliographic record
Abstract
BACKGROUND: The prevalence of cardiovascular disease is high among patients with chronic kidney disease and cardiovascular events (CVE) remain the leading cause of death after kidney transplantation (KT). We performed a retrospective analysis of 389 KT recipients to assess if the European Society of Cardiology Score (ESC-Score), Framingham Heart Study Score (FRAMINGHAM), Prospective Cardiovascular Munster Study Score (PROCAM-Score) or Assessing cardiovascular risk using Scottish Intercollegiate Guidelines Network Score (ASSIGN-Score) algorithms can predict cardiovascular risk after KT at the time of entering the waiting list. METHODS: 389 KT candidates were scored by the time of entering the waiting list. Pearsons chi-square test, cox regression analysis and survival estimates were performed to evaluate the reliability of the cardiovascular scoring models after successful KT. RESULTS: During a follow-up of 8 ± 5.8 years, 96 patients (30%) died due to cardiovascular problems, whereas 13.9% suffered non-fatal CVE. Graft loss occurred in 84 patients (21.6%). Predictors of CVE, survival and graft loss were age and the length of end-stage kidney disease. All scores performed well in assessing the risk for CVE (P < 0.01). Receiver-operating characteristic analysis using the ESC-SCORE, as an example, suggested a cut-off for risk stratification and clinical decisions. CONCLUSIONS: We found all tested scores were reliable for cardiovascular assessment. We suggest using cardiac scores for risk assessment before KT and then taking further steps according to current guidelines.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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".