A novel system for removing examinee’s exhaled air using an open, lightweight non-sealing facemask-a proof-of-concept study
Bibliographic record
Abstract
Patients with respiratory infections (e.g., COVID-19, antimicrobial resistant bacteria) discharge pathogens to the environment, exposing healthcare workers and inpatients to deleterious complications. This study tested the performance of SPEAR-P1 (synchronized personal exhaled air removal system - prototype 1), which actively detects expiration and removes exhaled air using an open, non-sealing lightweight facemask connected to a deep vacuum generating unit (DVGU). Fourteen healthy examinees practiced breathing through facemasks at 30, 25 and 20 breaths per minute; oxygen and nebulized saline were added at later steps. To test the efficacy of removing exhaled air, CO2 was used as a proxy and its level was measured from the outer surface of the open facemask. Compared to the baseline recording, SPEAR-P1 reduced CO2 escaping from the facemask by 66% on average for all study steps and respiratory rates (p<0.001), reaching 85.55% on average at 20 breaths per minute (p<0.001). This study shows that removing exhaled air from examinees using an open, non-sealing lightweight facemask is feasible. Future development of this system will enhance its efficacy and provide a method to remove a patient's contaminating aerosol without the need to "seal" the patient, especially in settings where isolation rooms are not readily available.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".