Combining donor derived cell free DNA and gene expression profiling for non‐invasive surveillance after heart transplantation
Bibliographic record
Abstract
BACKGROUND: Donor-derived cell free DNA (dd-cfDNA) and gene expression profiling (GEP) offer noninvasive alternatives to rejection surveillance after heart transplantation; however, there is little evidence on the paired use of GEP and dd-cfDNA for rejection surveillance. METHODS: A single center, retrospective analysis of adult heart transplant recipients. A GEP cohort, transplanted from January 1, 2015 through December 31, 2017 and eligible for rejection surveillance with GEP was compared to a paired testing cohort, transplanted July 1, 2018 through June 30, 2020, with surveillance from both dd-cfDNA and GEP. The primary outcomes were survival and rejection-free survival at 1 year post-transplant. RESULTS: In total 159 patients were included, 95 in the GEP and 64 in the paired testing group. There were no differences in baseline characteristics, except for less use of induction in the paired testing group (65.6%) compared to the GEP group (98.9%), P < .01. At 1-year, there were no differences between the paired testing and GEP groups in survival (98.4% vs. 94.7%, P = .23) or rejection-free survival (81.3% vs. 73.7% P = .28). CONCLUSIONS: Compared to post-transplant rejection surveillance with GEP alone, pairing dd-cfDNA and GEP testing was associated with similar survival and rejection-free survival at 1 year while requiring significantly fewer biopsies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".