Everolimus: efficacy and safety in cardiac transplantation
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Prognosis after cardiac transplantation continues to improve with long-term outcomes limited by malignancy and coronary allograft vasculopathy (CAV). Everolimus may potentially reduce these late term complications, while maintaining the low cellular rejection rates seen with standard therapy. AREAS COVERED IN THIS REVIEW: Multiple studies have demonstrated the efficacy of everolimus in reducing acute rejection in heart transplant patients, progression and development of CAV, and the prevention and treatment of common malignancies, including skin cancer and Kaposi sarcoma. This review re-examines these studies with a focus on patient tolerability and safety. WHAT THE READER WILL GAIN: Tolerability and safety of everolimus remain a concern with pneumonitis, effusions, mouth ulcers, edema and impaired wound healing associated with morbidity and mortality. Studies have repeatedly demonstrated renal function deterioration with concomitant everolimus and a standard dose calcineurin inhibitor (CNI). This impact can be partly reduced with CNI dose reduction without an increase in the rate of rejection. TAKE HOME MESSAGE: If future studies confirm reduced CAV and malignancy rates, everolimus will become an important agent in de novo and maintenance immunotherapy in cardiac transplantation. Patient centered immunotherapy is preferred to protocol-based immunotherapy as it allows tailoring of immune therapy to each individual patient's rejection risk and side profile.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 0.001 |
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".