Inhaled nitric oxide for the treatment of COVID-19: an open-label, parallel, randomised controlled trial
Bibliographic record
Abstract
Background: Inhaled nitric oxide (iNO) in high concentration inhibits SARS-CoV-2 replication in epithelial cells and may prevent severe disease in hospitalised patients. The aim of this study was to evaluate safety and efficacy of iNO 160 ppm on supplemental oxygen in patients hospitalised with COVID-19. Methods: We conducted an open-label, randomised clinical trial in hospitalised patients with COVID-19 receiving supplemental oxygen. Patients were randomly assigned to receive iNO for 6 h in addition to standard of care. The primary safety end-point was assessed by incidence of adverse events. The secondary efficacy end-point was the number of days free of supplemental oxygen within 15 days after randomisation. Results: 55 patients were enrolled, 27 in the iNO group and 27 in the control group, and one patient was excluded. No adverse events occurred with the inhalation of nitric oxide. Median (IQR) number of days free of supplemental oxygen was 11 (8.0-13.0) in the iNO group and 8 (2.5-10.5) in the control group (p=0.044). The iNO group had a shorter length of hospital stay (4.5 days (3.0-6.3) compared to 7.0 days (6.0-10.0) in the control group; p=0.004). Clinical score was lower in the iNO group on days 3 and 5 (p=0.010 and p=0.033). The number of patients weaned from ventilatory support on day 3 was higher in the iNO group (n=9 (33%)) when compared to controls (n=1 (4%)); p=0.005. Respiratory failure and mortality did not differ between the groups. Conclusion: The iNO treatment was well tolerated and safe. Among adults hospitalised for COVID-19, iNO 160 ppm for 6 h increased the number of days free from supplemental oxygen, shortened the days of ventilatory support and the length of hospital stay, and improved the clinical score.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".