Cyclosporin versus Tacrolimus as Primary Immunosuppressant After Liver Transplantation:A Meta-Analysis
Bibliographic record
Abstract
A systematic review of randomized clinical trials (RCT) was undertaken to evaluate the beneficial and harmful effects of immunosuppression with cyclosporin versus tacrolimus for liver transplanted patients. MEDLINE, EMBASE, Cochrane Central and Hepato-Biliary Group Controlled Trials Registers were searched. Using fixed and random effects model, relative risk (RR), values <1 favoring tacrolimus, with 95% confidence intervals (CI) were calculated. Of 717 potentially relevant references, 16 RCTs were eligible for inclusion. Mortality and graft loss at 1 year were significantly reduced in tacrolimus-treated recipients (Death: RR 0.85, 95% CI 0.73-0.99; graft loss: RR 0.73, 95% CI 0.61-0.86). Tacrolimus reduced the number of recipients with acute rejection (RR 0.81, 95% CI 0.75-0.88) and steroid-resistant rejection (RR 0.54, 95% CI 0.47-0.74) in the first year. Lymphoproliferative disorder or dialysis rates were not different but more de novo diabetes (RR 1.38, 95% CI 1.01-1.86) occurred with tacrolimus. More patients stopped cyclosporin than tacrolimus (RR 0.57, 95% CI 0.49-0.66). Treating 100 recipients with tacrolimus instead of cyclosporin would avoid rejection and steroid-resistant rejection in nine and seven patients respectively, graft loss and death in five and two patients respectively, but four additional patients would develop diabetes after liver transplantation.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".