Development and validation of the airway surgery enclosure for high-risk aerosol-generating airway procedures: a bench and clinical study
Bibliographic record
Abstract
Procedures on the upper airway in patients with respiratory viruses are considered to carry the greatest risk of infection spread to operating room personnel through aerosolization. Appropriate personal protective equipment must be worn, but availability varies worldwide and resources may be limited. We describe the development, validation, and safe implementation of a reusable enclosure with an inexpensive, acrylic design, for use in high-risk airway procedures. Examples of common yet high-risk, aerosol-generating procedures performed with the Airway Surgery Enclosure (ASE) include laryngo-bronchoscopy, suspension laryngoscopy for removal of airway lesions, and rigid bronchoscopy including airway foreign body removal. The ASE demonstrated an 87-94% reduction in aerosolized particle concentration compared to ambient room levels. Bench testing validated the containment capability through laser-based particle imaging and air sampling, while clinical evaluations confirmed ergonomic feasibility and usability. While the ASE provides significant reductions in aerosol exposure, implementation challenges include integration with existing operating room workflows, material durability over repeated sterilization cycles, and cost considerations for widespread adoption. Further studies are needed to assess long-term clinical effectiveness and user adaptability.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".