Graft dysfunction and cardiovascular risk--an unholy alliance
Bibliographic record
Abstract
Patients with progressive chronic renal failure on dialysis treatment and renal transplantation have an elevated mortality in comparison to the general population [1,2]. Despite the fact that renal transplant recipients are highly susceptible to infection and malignant disease, these patients mainly die of premature cardiovascular disease (CVD). Despite the fact that classical cardiovascular risk factors are highly prevalent in renal transplants, the Framingham heart score that considers patient's age, dislipidaemia, hypertension, diabetes and cigarette smoking as risk factors underestimates cardiovascular risk in these patients, suggesting that other factors may also play an important role [3]. These non-traditional cardiovascular risk factors include increased oxidative stress, elevated biomarkers of inflammation, anaemia, calcium-phosphate metabolism imbalance, hyperhomocystinaemia or left ventricular hypertrophy [4]. Furthermore, a link between degree of renal function and CVD in the general population and renal transplants has been demonstrated [5,6]. Patients with end-stage renal failure have a 10–20-fold increase in cardiovascular mortality compared to the general population, but patients who receive a kidney transplant have a better long-term survival than patients who remain on the kidney waiting list [1]. In a large longitudinal study of mortality in patients who were receiving long-term dialysis, the standardized mortality ratio for all patients on dialysis, patients on dialysis who were awaiting transplantation and patients who received a kidney transplant were 16.1, 6.3 and 3.8 per 100 patient-years, respectively [1].
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".