Induction therapy in heart transplantation: A systematic review and network meta‐analysis for developing evidence‐based recommendations
Bibliographic record
Abstract
INTRODUCTION: Induction therapy (IT) utility in heart transplantation (HT) remains contested. Commissioned by a clinical-practice guidelines panel to evaluate the effectiveness and safety of IT in adult HT patients, we conducted this systematic review and network meta-analysis (NMA). METHODS: We searched for studies from January 2000 to October 2022, reporting on the use of any IT agent in adult HT patients. Based on patient-important outcomes, we performed frequentist NMAs separately for RCTs and observational studies with adjusted analyses, and assessed the certainty of evidence using the GRADE framework. RESULTS: From 5156 publications identified, we included 7 RCTs and 12 observational studies, and report on two contemporarily-used IT agents-basiliximab and rATG. The RCTs provide only very low certainty evidence and was uninformative of the effect of the two agents versus no IT or one another. With low certainty in the evidence from observational studies, basiliximab may increase 30-day (OR 1.13; 95% CI 1.06-1.20) and 1-year (OR 1.11; 95% CI 1.02-1.22) mortality compared to no IT. With low certainty from observational studies, rATG may decrease 5-year cardiac allograft vasculopathy (OR .82; 95% CI .74-.90) compared to no IT, as well as 30-day (OR .85; 95% CI .80-.92), 1-year (OR .87; 95% CI .79-.96), and overall (HR .84; 95% CI .76-.93) mortality compared to basiliximab. CONCLUSION: With low and very low certainty in the synthetized evidence, these NMAs suggest possible superiority of rATG compared to basiliximab, but do not provide compelling evidence for the routine use of these agents in HT recipients.
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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 | 0.075 | 0.196 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.029 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".