Reliability of Donor Lung Sampling in Lung Transplantation
Bibliographic record
Abstract
Introduction: Ex vivo lung perfusion (EVLP) is a normothermic platform used to assess donor lungs. Many have studied biomarkers in lung injury, but it is unclear whether samples taken from one location are representative of the organ. Our objective was to investigate the uniformity of cytokine expression in tissue biopsies and in EVLP perfusates from various locations. Methods: In the tissue study, eight donor lungs were partitioned from apex to base. In each lung, three biopsies were taken from the third, sixth, and ninth slices, while two were taken from the lingula and an injury site. In the perfusate study, four samples were taken from four lobes in eight donors during EVLP. Expressions of IL-6, IL-8, IL-10, and IL-1β were measured using qPCR and ELISA. Results: In the tissue study, the mean intra-biopsy equal-variance F-value was 0.53. The median intra-biopsy coefficient of variation (CV) was 18%. In donors without gross focal injury, the mean comparisons of biopsies in each donor showed that the three consistent slices showed no differences and had a CV of 20%, which was similar to the intra-biopsy CV (p=0.80). Both the lingula and injury biopsies demonstrated larger differences from the rest. The median intra-lung CV of perfusates from different lobes was 4.9%. Conclusion: Intra-biopsy variances were consistent across biopsies. Lungs without gross focal injury demonstrated more consistent gene expression. The lingula is not a representative site due to high signal variability. The consistent measurements in EVLP perfusates provided a uniform picture of the inflammation.
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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 | 0.039 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".