m-TOR inhibitors: What role in liver transplantation?
Bibliographic record
Abstract
The development of calcineurin inhibitors (CNIs) led to marked improvements in patient and graft survival after liver transplantation (LTx). We have been left, however, with a dependence on immunosuppressive agents with nephrotoxicity, neurotoxicity, adverse impacts on cardiac risk profile, and risk for malignancy. These challenges need to be met against a dominance of hepatitis C virus (HCV) and hepatocellular carcinoma (HCC) as indications for liver transplant. Unmet needs for immunosuppression (IS) in LTx include: (1) Effective drugs that avoid CNIs toxicities. (2) Agents without adverse impact on HCV recurrence. (3) Compounds that minimize risk of HCC recurrence. New immunosuppressives will need to address the above needs while supporting patient and graft survival equivalent to those achievable with CNIs, ideally without important new toxicities. Two new classes of agents are currently in advanced clinical development: belatacept, and the mammalian target of rapamycin inhibitors (m-TORi). This manuscript will review evidence for a role for m-TORi in LTx in a range of clinical scenarios including patients with CNI nephrotoxicity or neurotoxicity, patients at risk of (or with) HCV recurrence, and patients at risk of HCC recurrence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".