Bibliographic record
Abstract
PURPOSE OF REVIEW: Renal dysfunction due to calcineurin inhibitor (CNI) toxicity is a major clinical problem in cardiac transplantation. The aim of the article is to review the efficacy and safety of various renal sparing strategies in cardiac transplantation. RECENT FINDINGS: Small studies have documented that late initiation of CNI is safe in patients treated with induction therapy at the time of transplantation. Use of mycophenolate is superior when compared with azathioprine to allow for CNI reduction. More substantial reduction in CNI levels is safe and effective with the introduction of sirolimus or everolimus. However, studies that use very early CNI discontinuation have found an increased risk of allograft rejection, and this strategy requires further study before it can be routinely recommended. CNI discontinuation late after cardiac transplantation seems more effective than CNI reduction in terms of preserving renal function. Patients with longstanding CNI treatment or proteinuria are less likely to respond favourably to a switch from a CNI-based regimen to a proliferation signal inhibitor-based regimen. SUMMARY: Each cardiac transplant recipient with renal dysfunction must be individually evaluated with respect to degree of renal dysfunction, proteinuria and rejection risk and a renal sparing strategy chosen accordingly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.004 | 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".