New developments in immunosuppressive therapy in renal transplantation
Bibliographic record
Abstract
The introduction of new immunosuppressive agents and protocols has improved outcomes for renal transplant recipients by decreasing the risk of rejection and by increasing the function and lifespan of the allograft. This article reviews the major changes in the combinations of therapies used: calcineurin inhibitors, target of rapamycin inhibitors, mycophenolate mofetil, non-depleting monoclonal versus depleting monoclonal and polyclonal antibodies for induction and increasing emphasis on protocols for reduction or avoidance of steroids and calcineurin inhibitors. The new agents with novel immunological targets such as anti-CD40 ligand, LEA29Y, FTY720, anti-CD20 (rituximab, Rituxan, Mabthera) and anti-CH52 (alemtuzumab, Campath), which are under development but have yet to survive the rigors of clinical trials are also discussed. In the presence of low early rejection rates, immunosuppressive therapy is setting new goals such as better graft function (glomerular filtration rates), reduction in adverse effects such as hypertension, hyperlipidaemia and drug toxicity and, above all, the prevention of late graft deterioration.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".