Effectiveness of T cell–mediated rejection therapy: A systematic review and meta-analysis
Bibliographic record
Abstract
The effectiveness of T cell–mediated rejection (TCMR) therapy for achieving histological remission remains undefined in patients on modern immunosuppression. We systematically identified, critically appraised, and summarized the incidence and histological outcomes after TCMR treatment in patients on tacrolimus (Tac) and mycophenolic acid (MPA). English-language publications were searched in MEDLINE (Ovid), Embase (Ovid), Cochrane Central (Ovid), CINAHL (EBSCO), and Clinicaltrials.gov (NLM) up to January 2021. Study quality was assessed with the National Institutes of Health Study Quality Tool. We pooled results using an inverse variance, random-effects model and report the binomial proportions with associated 95% confidence intervals (95% CI). Statistical heterogeneity was explored using the I2 statistic. From 2875 screened citations, we included 12 studies (1255 participants). Fifty-eight percent were good/high quality while the rest were moderate quality. Thirty-nine percent of patients (95% CI 0.26–0.53, I2 77%) had persistent ≥Banff Borderline TCMR 2–9 months after anti-rejection therapy. Pulse steroids and augmented maintenance immunosuppression were mainstays of therapy, but considerable practice heterogeneity was present. A high proportion of biopsy-proven rejection exists after treatment emphasizing the importance of histology to characterize remission. Anti-rejection therapy is foundational to transplant management but well-designed clinical trials in patients on Tac/MPA immunosuppression are lacking to define the optimal therapeutic approach.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".