Laryngeal ultrasound to observe laryngeal movements during non-invasive ventilation in COPD
Bibliographic record
Abstract
Background: Transnasal fiberoptic laryngoscopy (TFL) has shown that laryngeal obstruction can occur during non-invasive ventilation (NIV), potentially compromising the effectiveness of assisted ventilation. Although TFL provides valuable insights, it is considered an invasive procedure. Laryngeal ultrasound (US) offers a feasible, non-invasive bedside alternative. Aims: To compare inspiratory laryngeal observations and patient reported discomfort during TFL vs simultaneous laryngeal US. Method: An explorative observational study of 15 patients with stable hypercapnic COPD. Laryngeal responses were examined using simultaneous video-recorded TFL and laryngeal US during NIV titration; i.e. incrementally increase inspiratory positive pressure (IPAP) to the patient's device limit. Laryngeal US was repeated for anterior and lateral approaches. The patients rated discomfort using a numeric rating scale (NRS) from 0-10. A single examiner retrospectively assessed and scored inspiratory laryngeal responses from video recordings of both examination methods at each IPAP level, and the findings were compared. Results: All participants were successfully assessed using TFL and 11/15 using laryngeal US. A total of 86 IPAP levels were successfully assessable with TFL, and 50/86 with laryngeal US. In 22 US probe placements the laryngeal assessment was not possible due to different reasons. A median (range) NRS for TFL was 1 (0-3) and for laryngeal US 0 (0-1). Assessments made with these two methods agreed in all assessable IPAP levels. Conclusion: With high agreement to TFL, laryngeal US is a well-tolerated, non-invasive alternative for assessing laryngeal movements during NIV titration.
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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 | 0.002 | 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".