Prevention of submicron aerosolized particle dispersion: evaluation of an aerosol box using a pediatric simulation model
Bibliographic record
Abstract
Background and Aim: The SplashGuard CG (SG) is a barrier enclosure developed to protect healthcare workers from SARS-CoV-2 transmission during aerosol-generating procedures. Our objective was to evaluate the protection provided by the SG against aerosolized particles (AP), using a pediatric simulation model of spontaneous ventilation (SV) and noninvasive ventilation (NIV). Methods: An aerosol generator was connected to the airways of a pediatric high-fidelity manikin with a breathing simulator. AP concentrations were measured both in SV and NIV in the following conditions: with and without SG, inside and outside the SG, with and without suction applied to the device. Results: In the SV simulated setting, AP peaks were lower with SG: 0.1 × 105 particles/L compared to without: 1.6 × 105, only when the ports were closed and suction applied. In the NIV simulated setting, AP peaks outside the SG were lower than without SG (20.5 × 105 particles/L), whatever the situation, without suction (14.4 × 105particles/L), with suction and ports open or closed: 10.3 and 0.7 × 105 particles/L. In SV and NIV simulated settings, the AP peaks measured within the SG were much higher than the AP peaks measured without SG, even when suction was applied to the device. Conclusions: The SG seems to decrease peak AP exposure in the 2 ventilation contexts, but only with closed port and suction in SV. However, high concentrations of AP remain inside even with suction and SG should be used cautiously.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".