Laryngeal observations during non-invasive ventilation titration in chronic obstructive pulmonary disease
Bibliographic record
Abstract
Background: High-pressure non-invasive ventilation (NIV) is used in chronic obstructive pulmonary disease (COPD) to normalize hypercapnia, thereby enhancing quality of life and improving survival. Despite the benefits, treatment adherence poses challenges. We hypothesized that adverse upper airway responses during NIV may influence treatment efficacy and patient outcomes. Aims: To visualise and describe the laryngeal responses to NIV in individuals with COPD. Methods: An explorative observational study including awake individuals with stable hypercapnic COPD. Laryngeal responses were examined with video-recorded transnasal fiberoptic laryngoscopy during NIV treatment, using a predefined titration protocol starting from each participant’s individual settings up to the inspiratory pressure (IPAP) limit of the patients’ device (25 or 30 cmH2O). The laryngeal responses during NIV were assessed from video-recordings. Results: Eight participants with COPD were included, baseline IPAP levels ranging from 12-26 cmH2O. Among participants where the device’s upper IPAP limit was 25 cmH2O (n=4; baseline IPAP 12-22), 3/4 had normal laryngeal responses. In participants using a device with an upper limit of 30 cmH2O (n=4; baseline IPAP 14-26) laryngeal adduction was observed at high pressures in all. Conclusion: Laryngeal responses to NIV in hypercapnic stable COPD were heterogeneous, with a tendency for increased IPAP to induce laryngeal adduction. Further studies are warranted, using NIV devices capable of delivering higher pressures, to ascertain whether laryngeal obstruction occurs exclusively at pressures exceeding 25 cmH2O.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".