A bench study of inhaled nitric oxide delivery during high frequency percussive ventilation
Bibliographic record
Abstract
Abstract Background Safe and effective delivery of inhaled nitric oxide (INO) requires the appropriate interface of ventilator and INO delivery device. Methods We compared INO delivery using four configurations with the Transport Sinusoidal Bronchotron® and INOmax DSIR Plus® in a lung model. Ventilator settings and lung model values were held constant. Delivered NO, NO2, and inspired oxygen (FIO2) were measured. The mean difference between set and measured NO was calculated and compared using ANOVA. Results Placement of the injector module in line with the sliding venturi resulted in a ventilator failure. With both continuous flow techniques there was no appreciable NO2 generated and the mean difference between set NO and measured NO at 20 and 40 ppm was −16.5 ppm and −33.2 ppm at flows of 5 and 10 L/min. Placement of the injector module between the sliding venturi and lung model resulted in an increase of NO2 to a peak of 2.4 ppm (mean 2.3 + 0.1) and a mean difference between set and measured NO of + 11.3 ppm and +30 ppm at 20 and 40 ppm, 300 cycles per minute (cpm), and 22.1 ppm and 37.6 ppm, at 20 and 40 ppm, 600 cpm. None of the test configurations delivered INO within 30% of set concentrations. No alarms or interruption of INO delivery occurred. Conclusion The dual gas delivery system of the Bronchotron prevents accurate delivery of INO. The combination of these two devices should be accomplished with caution and vigilance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".