Use of induction therapy post‐heart transplantation: Clinical practice recommendations based on systematic review and network meta‐analysis of evidence
Bibliographic record
Abstract
BACKGROUND: The use of induction therapy (IT) agents in the early post-heart transplant period remains controversial. The following recommendations aim to provide guidance on the use of IT agents, including Basiliximab and Thymoglobulin, as part of routine care in heart transplantation (HTx). METHODS: We recruited an international, multidisciplinary panel of 15 stakeholders, including patient partners, transplant cardiologists and surgeons, nurse practitioners, pharmacists, and methodologists. We commissioned a systematic review on benefits and harms of IT on patient-important outcomes, and another on patients' values and preferences to inform our recommendations. We used the GRADE framework to summarize our findings, rate certainty in the evidence, and develop recommendations. The panel considered the balance between benefits and harms, certainty in the evidence, and patient's values and preferences, to make recommendations for or against the routine post-operative use of Thymoglobulin or Basiliximab. RESULTS: The panel made recommendations on three major clinical problems in HTx: (1) We suggest against the routine post-operative use of Basiliximab compared to no IT, (2) we suggest against the routine use of Thymoglobulin compared to no IT, and (3) for those patients for whom IT is deemed desirable, we suggest for the use of Thymoglobulin as compared to Basiliximab. CONCLUSION: This report highlights gaps in current knowledge and provides directions for clinical research in the future to better understand the clinical utility of IT agents in the early post heart transplant period, leading to improved management and care.
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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 | 0.062 | 0.215 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".