Introducing helmet non-invasive ventilation during COVID-19 pandemic: Early experience of two centres
Bibliographic record
Abstract
Purpose: The helmet is a novel interface for delivering non-invasive ventilation (NIV). We conducted a case series to characterize introduction of the helmet interface in both COVID and non-COVID patients at two-centres. Methods: We enrolled all patients with respiratory failure admitted to the Juravinski Hospital (Hamilton, Canada) and St. Joseph's Health Center (Syracuse, New York) between November 1, 2020 and June 30, 2021 who used the helmet interface (Intersurgical StarMed) as part of this introduction into clinical practice. We collected patient demographics, reason for respiratory failure, NIV settings, device-related complications and outcomes. We report respiratory therapist's initial experiences with the helmet using descriptive results. Results: We included 16 patients with a mean age of 64.3 ± 10.9 years. The most common etiology for respiratory failure was pneumonia (81.3%). The median duration of NIV during the ICU admission was 67.5 (15.3, 80.8) hours, with a mean maximum PS of 13.9 ± 6.6 cm H2O and a mean maximum PEEP of 10.4 ± 5.1 cm H20. Three patients (18.7%) did not tolerate the helmet. Ten (62.5%) patients ultimately required intubation, and 7 (43.4%) patients died while in the ICU. The most common reason for intubation was worsening hypoxia (70%). No adverse events related to the helmet were recorded. Conclusion: Over the 8-month period of this study, we found that the helmet was well tolerated in over 80% of patients, although, more than half ultimately required intubation. Randomized controlled trials with this device are required to fully assess the efficacy of this interface.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".