Reducing Adverse Effects of Immunosuppressive Agents in Kidney Transplant Recipients
Bibliographic record
Abstract
Success in solid organ transplantation with minimal complications can now be achieved for most patients, and a remarkable rate of graft and patient survival can also be expected. However, the potential for adverse events and comorbid conditions increases with longer graft survival. Although the immunosuppressive regimen is central to the outcome of the transplant recipient and directly impacts the survival of the graft, chronic use of immunosuppressive agents is associated with metabolic disturbances such as hypertension, hyperlipidemia, loss of bone density, nephrotoxicity, and diabetes, which may contribute to other comorbid conditions. In addition, changes in appearance, gingival hyperplasia, hirsutism, alopecia, and weight gain disrupt quality of life and may lead to noncompliance with the immunosuppressive regimen. New immunosuppressive medications, including mycophenolate mofetil, sirolimus, basiliximab, and daclizumab, have allowed for experimentation with new regimens designed to reduce or allow discontinuation of corticosteroids and calcineurin inhibitors. This review highlights the impact and cost of immunosuppressive side effects and the potential for new immunosuppressive regimens to reduce this substantial clinical burden in transplantation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".