High-flow nasal oxygen versus noninvasive ventilation in adult patients with cystic fibrosis: a randomized crossover physiological study
Bibliographic record
Abstract
Noninvasive ventilation (NIV) is the first-line treatment of adult patients with exacerbations of cystic fibrosis (CF). High-flow nasal oxygen therapy (HFNT) might benefit patients with hypoxemia and can reduce physiological dead space. We hypothesized that HFNT and NIV would similarly reduce work of breathing and improving breathing pattern in CF patients. Our objective was to compare the effects of HFNT versus NIV in terms of work of breathing, assessed noninvasively by the thickening fraction of the diaphragm (TFdi, measured with ultrasound), breathing pattern, transcutaneous CO 2 (PtcCO 2 ), hemodynamics, dyspnea and comfort. Adult CF patients who had been stabilized after requiring ventilatory support for a few days were enrolled and ventilated with HFNT and NIV for 30 min in crossover random order. Fifteen patients were enrolled. Compared to baseline, HFNT, but not NIV, reduced respiratory rate (by 3 breaths/min, p = 0.01) and minute ventilation (by 2 L/min, p = 0.01). Patients also took slightly larger tidal volumes with HFNT compared to NIV ( p = 0.02). TFdi per breath was similar under the two techniques and did not change from baseline. MAP increased from baseline with NIV and compared to HFNT ( p ≤ 0.01). Comfort was poorer with the application of both HFNT and NIV than baseline. No differences were found for heart rate, SpO 2 , PtcCO 2 or dyspnea. In adult CF patients stabilized after indication for ventilatory support, HFNT and NIV have similar effects on diaphragmatic work per breath, but high-flow therapy confers additional physiological benefits by decreasing respiratory rate and minute ventilation. Ethics Committee of St. Michael’s Hospital (REB #14-338) and clinicaltrial.gov (NCT02262871).
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".