Time since liver transplant and immunosuppression withdrawal outcomes: Systematic review and individual patient data meta‐analysis
Bibliographic record
Abstract
BACKGROUND & AIMS: Successful immunosuppression withdrawal (ISW) is possible for a subfraction of liver transplant (LT) recipients but the factors that define the risk of ISW failure are largely unknown. One candidate prognostic factor for ISW success or operational tolerance (OT) is longer time between LT and ISW which we term "pre-withdrawal time". To clarify the impact of pre-withdrawal time span on subsequent ISW success or failure, we conducted a systematic review with meta-analysis. METHODS: We systematically interrogated the literature for LT recipient ISW studies reporting pre-withdrawal time. Eligible articles from Embase, Medline, and the Cochrane Central Register of Controlled Trials were used for backward and forward citation searching. Pre-withdrawal time individual patient data (IPD) was requested from authors. Pooled mean differences and time-response curves were calculated using random-effects meta-analyses. RESULTS: We included 17 studies with 691 patients, 15 of which (620 patients) with IPD. Study-level risk of bias was heterogeneous. Mean pre-withdrawal time was greater by 427 days [95% confidence interval (CI) 67-788] in OT compared to non-OT patients. This increase was potentiated to 799 days (95% CI 369-1229) or 1074 days (95% CI 685-1463) when restricting analysis to adult or European study participants. In time-response meta-analysis for adult or European ISW candidates, likelihood of OT increased by 7% (95% CI 4-10%) per year after LT (GRADE low- and moderate-certainty of evidence, respectively). CONCLUSIONS: Our data support the impact of pre-withdrawal time in ISW decision-making for adult and European LT recipients. PROSPERO REGISTRATION: CRD42021272995.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.046 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".