Proliferation signal inhibitors in cardiac transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Standard immunosuppression after cardiac transplantation includes a calcineurin inhibitor in combination with mycophenolate mofetil or azathioprine and corticosteroids. These agents have led to excellent outcomes but have shortcomings in terms of efficacy and toxicity. A new class of immunosuppressants, proliferation signal inhibitors, may meet some of these shortcomings. RECENT FINDINGS: The efficacy of the available proliferation signal inhibitors - sirolimus and its derivative everolimus - has been compared with azathioprine in three randomized clinical trials. Sirolimus or everolimus use was associated with lower rates of acute rejection and reduced development of chronic allograft vasculopathy. Sirolimus was not found to be superior to mycophenolate mofetil in a randomized trial. Proliferation signal inhibitors have been reported to be effective in refractory recurrent acute rejection. Nonrandomized studies have demonstrated that proliferation signal inhibitor-based immunosuppression enables recovery from renal dysfunction secondary to calcineurin inhibitor treatment. Proliferation signal inhibitor-based treatment is associated with a lower risk of malignancy than calcineurin inhibitor-based regimens. Proliferation signal inhibitors have significant adverse effects that may limit widespread use. SUMMARY: Proliferation signal inhibitors are important new immunosuppressive agents that have added considerably to the armamentarium allowing further tailored immunosuppression to individualize patient care after heart transplantation.
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) | 0.005 | 0.002 |
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".