Assessment and Management of Hypertension in Transplant Patients
Bibliographic record
Abstract
Hypertension in renal transplant recipients is common and ranges from 50% to 80% in adult recipients and from 47% to 82% in pediatric recipients. Cardiovascular morbidity and mortality and shortened allograft survival are important consequences of inadequate control of hypertension. In this review, we examine the epidemiology, pathophysiology, and management considerations of post-transplant hypertension. Donor and recipient factors, acute and chronic allograft injury, and immunosuppressive medications may each explain some of the pathophysiology of post-transplant hypertension. As observed in other patient cohorts, renal artery stenosis and adrenal causes of hypertension may be important contributing factors. Notably, BP treatment goals for renal transplant recipients remain an enigma because there are no adequate randomized controlled trials to support a benefit from targeting lower BP levels on graft and patient survival. The potential for drug-drug interactions and altered pharmacokinetics and pharmacodynamics of the different antihypertensive medications need to be carefully considered. To date, no specific antihypertensive medications have been shown to be more effective than others at improving either patient or graft survival. Identifying the underlying pathophysiology and subsequent individualization of treatment goals are important for improving long-term patient and graft outcomes in these patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".