Cell-free DNA in ex-vivo lung perfusate is associated with low-quality lungs and lung transplant outcome
Bibliographic record
Abstract
BACKGROUND: Cell-free DNA (cfDNA) in ex-vivo lung perfusion (EVLP) perfusate has been shown to potentially reflect lung injury; however, the relationship between cfDNA concentration with clinical EVLP lung outcomes has not been elucidated. METHODS: A discovery cohort of n = 100 clinical EVLP cases and a validation cohort (n = 50) were used in this single-center, retrospective cohort study. cfDNA was extracted and quantified from perfusate samples. The concentration of cfDNA at 1 hour and the change in cfDNA concentration per hour of EVLP in the transplanted and declined groups were compared by univariable and multivariable logistic regression. cfDNA was introduced as an additional factor in a machine-learning algorithm to predict lung utilization and postoperative outcome and the performance evaluated. RESULTS: Significantly higher cfDNA concentrations were observed in the declined group than in the transplanted group (1 hour: p < 0.001; delta/hour: p = 0.031). Multivariable analysis among the 1 hour factors showed that [cfDNA 1 hour] (OR 4.27, p = 0.010) was an independent prognostic factor. Increases in [cfDNA 1 hour], [cfDNA delta/hour], and both showed that both initial [cfDNA] and increases in [cfDNA] over time were independently correlated with the probability of a lung being declined. The validation analysis also confirmed higher [cfDNA 1 hour] in the declined group than in the transplanted group (p = 0.010). Addition of [cfDNA] features improved the performance of a machine-learning algorithm used to predict donor lung utilization. CONCLUSIONS: The cfDNA concentration in EVLP perfusate correlates with the rate of decline of lungs for transplant from EVLP.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".