Orthotopic liver transplantation using low-dose tacrolimus and sirolimus
Bibliographic record
Abstract
Although sirolimus (SRL) binds the immunophilin FK506-binding protein-12 (FKBP-12) with greater avidity than tacrolimus (TAC), animal studies have shown that SRL and TAC act synergistically to prevent rejection. Dose-related toxicity is more often the cause of TAC discontinuation than rejection. We hypothesized that SRL would allow for a substantial reduction in the concomitant dose of TAC after liver transplantation to levels less than the threshold for toxicity. A series of 56 liver transplant recipients were administered a combination of SRL and TAC (target trough levels, 7 and 5 ng/mL, respectively). Planned weaning of steroids commenced after 3 months. Pharmacokinetic (PK) studies were undertaken. Patient and graft survival were 52 patients (93%) and 51 grafts (91%), with a follow-up of 23 months (range, 6 to 35 months). One episode (1.8%) of hepatic artery thrombosis was seen. The rate of acute cellular rejection was 14%. No extra treatment was administered in 3 of 8 patients, and the other 5 episodes responded to a single course of steroids. Cytomegalovirus infection occurred in 4 patients (7%). Renal function, glucose control, and lipid metabolism are near normal in 47 patients (84%) without additional medication. Steroid elimination is completed in 51 patients (91%). Bioavailability of SRL and TAC varied between transplant recipients, but trough levels strongly correlated with the area under the curve (r(2) = 0.82 and r(2) = 0.84, respectively). Simultaneous administration did not affect the PK profile of the drugs at this dose. The ratio of trough level to daily dose correlated between SRL and TAC. The synergistic effect seen in animal models also occurs in clinical liver transplant recipients on SRL-TAC combination immunosuppression. A low-dose combination of SRL and TAC should be compared with conventional immunosuppression in a multicenter, randomized, controlled trial.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".