A Randomized, Multicenter, Blinded Pilot Study Assessing the Effects of Gaseous Nitric Oxide in an Ex Vivo System of Human Lungs
Bibliographic record
Abstract
Normothermic ex vivo lung perfusion (EVLP) is used to evaluate and condition donor lungs for transplantation. The objective of this study was to determine whether administration of exogenous nitric oxide during EVLP contributes to improvement of lung health. A multicenter, blinded, two-arm, randomized pilot study evaluated the effect of gaseous nitric oxide (gNO) administered during EVLP on donor lungs rejected for transplantation. gNO introduced into the perfusate at 80 parts per million (ppm) was compared with perfusate alone (P). An open-label substudy assessed inhaled nitric oxide gas (iNO) delivered into the lungs at 20 ppm via a ventilator. Primary endpoints were an aggregate score of lung physiology indicators and total duration of stable EVLP time. Secondary endpoints included assessments of lung weight and left atrium partial pressure of oxygen (LAPO 2 ). Twenty bilateral donor lungs (blinded study, n = 16; open-label substudy, n = 4) from three centers were enrolled. Median (min, max) total EVLP times for the gNO, P, and iNO groups were 12.4 (8.6, 12.6), 10.6 (6.0, 12.4), and 12.4 (8.7, 13.0) hours, respectively. In the blinded study, median aggregate scores were higher in the gNO group compared to the P group at most time points, suggesting better lung health with gNO (median score range [min, max], 0–3.5 [0, 7]) vs. P (0–2.0 [0, 5] at end of study). In the substudy, median aggregate scores did not improve for lungs in the iNO group. However, both the gNO and iNO groups showed improvements in lung weight and LAPO 2 compared to the P group. The data suggest that inclusion of gNO during EVLP may potentially prolong duration of organ stability and improve donor lung health, which warrants further investigation.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".