Letter by Amadio et al Regarding Article, “Cell-Free DNA to Detect Heart Allograft Acute Rejection”
Bibliographic record
Abstract
We commend Agbor-Enoh et al 1 on their interesting article, "Cell-Free DNA to Detect Heart Allograft Acute Rejection, " further supporting the role of donorderived cell-free DNA (ddcfDNA) as an early marker of acute rejection in heart transplantation.Given the wellarticulated limitations of endomyocardial biopsy as the gold standard test for diagnosing rejection, ddcfDNA may be an important noninvasive tool to minimize issues with interrater reliability for significant rejection, thereby affecting the overall immunosuppression strategy following transplantation.2 Before adopting a wholescale change in our approach to surveillance for acute rejection, we also wish to highlight areas for further clarification with respect to the analysis for this study.The authors paired ddcfDNA with endomyocardial biopsy to assess test performance.These data are then used to inform an alternate narrative suggesting that ddcfDNA predates biopsy-confirmed rejection.Are the authors suggesting that ddcfDNA is the gold standard assessment for acute rejection?This article would have benefited from showing individual trajectories of %ddcfDNA, as opposed to aggregate data.Without individual trajectories, it is impossible to rule out the presence of a potential bias in the group progression.The use of the generalized estimating equation model correctly accounts for within-subject correlation.However, the P values derived from the generalized estimating equation model reflect average differences between groups and not differences across pooled medians as is implied in Figure 3. 1 Could the authors state the correlation structure used for the generalized estimating equation model because this can affect the interpretation of results? 3 The number of patients with donor-specific antibodies in the study is also unclear.Because these patients have an increased incidence of acute rejection, it would be interesting to compare %ddcfDNA in patients with and without donor-specific antibodies.How do the authors
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.022 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".