Evaluation of Renal Function in Liver Transplant Recipients Receiving Daclizumab (Zenapax), Mycophenolate Mofetil, and a Delayed, Low-Dose Tacrolimus Regimen Vs. a Standard-Dose Tacrolimus and Mycophenolate Mofetil Regimen
Bibliographic record
Abstract
Posttransplant chronic renal failure, secondary to calcineurin inhibitor agents, is emerging as a major problem in liver transplantation. We report a randomized clinical trial comparing daclizumab, delayed low-dose tacrolimus (target trough level 4-8 ng/mL, starting day 4-6), Investigational Arm (n = 72), to standard tacrolimus induction/maintenance dosing, Standard Arm (n = 76), with mycophenolate mofetil and tapering corticosteroids in both study arms. The end-points were renal function indicated by the Modification of Diet in Renal Disease (MDRD). There was no significant difference in patient survival (86.6% Investigational Arm vs. 92.9% Standard Arm; P = 0.21) or acute rejection (23.2% vs. 27.7%, respectively; P = 0.68). Statistically significant differences in median glomerular filtration rate (GFR) were found in favor of the Investigational Arm. With the CG equation, the GFR at the end of the first week was 110.7 vs. 89.6 mL/min (P = 0.019) without significant differences thereafter. With the MDRD, statistically significant differences extended to the first posttransplant month (86.8 vs. 70.1 mL/min/1.73 m(2); P < 0.001) with and was seen at month 6 (75.4 vs. 69.5 mL/min/1.73 m(2); P = 0.038). In conclusion, delayed low-dose tacrolimus, in combination with daclizumab and mycophenolate mofetil, preserves early renal function post-liver transplantation without the cost of increased acute rejection.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".