Inadequacy of cardiovascular risk factor management in chronic kidney transplantation – evidence from the <scp>FAVORIT</scp> study
Bibliographic record
Abstract
BACKGROUND: Kidney transplant recipients (KTRs) have increased risk of cardiovascular disease (CVD). Our objective is to describe the prevalence of CVD risk factors applying standard criteria and use of CVD risk factor-lowering medications in contemporary KTRs. METHODS: The Folic Acid for Vascular Outcome Reduction in Transplantation study enrolled and collected medication data on 4107 KTRs with elevated homocysteine and stable graft function an average of five yr post-transplant. RESULTS: CVD risk factors were common (hypertension or use of blood pressure (BP) lowering medication in 92%, borderline or elevated low-density lipoprotein (LDL) or use of lipid-lowering agent in 66%, history of diabetes mellitus in 41%, and obesity in 38%); prevalent CVD was reported in 20% of study participants. National Kidney Foundation BP guidelines (BP <130/80 mmHg) were not met by 69% of participants. Uncontrolled hypertension (BP of 140/90 mmHg or higher) was present in 44% of those taking antihypertension medication; 18% of participants had borderline or elevated LDL, of which 60% were untreated, and 31% of the participants with prevalent CVD were not using an antiplatelet agent. CONCLUSION: There is opportunity to improve treatment and control of traditional CVD risk factors in kidney transplant 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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".