Maximizing the clinical outcome with mTOR inhibitors in the renal transplant recipient: defining the role of calcineurin inhibitors
Bibliographic record
Abstract
The synergistic action of mTOR inhibitors and calcineurin inhibitors (CNIs) provide a rationale for combination therapy, with the potential for CNI-dose reduction and corresponding clinical benefits. CNI therapy is necessary in the early post-transplant phase to deliver sufficient immunosuppressive potency, but use of standard-dose cyclosporine (CsA) with either sirolimus or everolimus has been associated with inferior renal function. Withdrawal of CsA from an mTOR-based regimen reduces renal toxicity, but this may be achieved at the price of increased late rejection and sirolimus-related adverse events. Use of a concentration-controlled mTOR inhibitor with low-exposure CsA seems to be effective in preventing rejection with good renal function. Currently, routine withdrawal of CNIs from an mTOR-inhibitor based regimen, or substitution of an mTOR inhibitor for a CNI, is not justified except in patients who experience toxicity (particularly nephrotoxicity) and who do not respond to CNI dose optimization.
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 | 0.000 |
| 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) | 0.001 | 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".