Kidney Transplant Recipients' Perspectives on Cardiovascular Disease and Related Risk Factors After Transplantation: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is a major cause of mortality among kidney transplant recipients (KTRs). These patients have a high prevalence of risk factors, such as hypertension, diabetes, and dyslipidemia. Despite regular medical care, few of them reach the recommended therapeutic targets. The objective of this study is to describe KTRs' perspectives on CVD and related risk factors, as well as their priorities for posttransplant care. METHODS: Twenty-six KTRs participated in a semistructured interview about their personal experience and offered their perspectives on CVD risk factors posttransplant. The interview was digitally recorded and the transcripts were analyzed using a thematic and content methodology. RESULTS: CVD and related risk factors appear to be underestimated and trivialized. Only 2 of 26 patients identified CVD prevention and treatment as a priority. The most important posttransplant priorities identified by patients were related to immunosuppressive drugs (13 of 26), posttransplant follow-up (10) and graft survival (9). However, 21 of 26 patients stated they wanted to be better informed about posttransplant CVD risk factors. CONCLUSIONS: CVD and related risk factors are not a priority for KTRs, and the importance of CVD is underestimated and trivialized. KTRs did recommend that tailored information be provided by various professionals and at several points in the transplantation process. This knowledge will help us develop a new approach to increase awareness of posttransplant CVD and related risk factors.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".