Effectiveness of SplashGuard Caregiver prototype in reducing the risk of aerosol transmission in intensive care unit rooms of SARS-CoV-2 patients: a prospective and simulation study
Bibliographic record
Abstract
BACKGROUND: The contagiousness of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is known to be linked to the emission of bioaerosols. Thus, aerosol-generating procedures (AGPs) could increase the risk of infection among healthcare workers (HCWs). AIM: To investigate the impact of an aerosol protection box, the SplashGuard Caregiver (SGGC) with suction system, by direct analysis of the presence of viral particles after an AGP, and by using the computational fluid dynamics (CFD) simulation method. METHODS: This prospective observational study investigated HCWs caring for patients with SARS-CoV-2 admitted to an intensive care unit (ICU). Rooms were categorized as: SGCG present and SGCG absent. Virus detection was performed through direct analysis, and using a CFD model to simulate the movement dynamics of airborne particles produced by a patient's respiratory activities. FINDINGS: Of the 67 analyses performed, three samples tested positive on quantitative polymerase chain reaction: one of 33 analyses in the SCCG group (3%) and two of 34 analyses in the non-SGCG group (5.9%). CFD simulations showed that: (1) reduction of the gaps of an SGCG could decrease the number of emitted particles remaining airborne within the room by up to 70%; and (2) positioning HCWs facing the opposite direction to the main air flow would reduce their exposure. CONCLUSIONS: This study documented the presence of SARS-CoV-2 among HCWs in a negative pressure ICU room of an infected patient with or without the use of an SGCG. The simulation will help to improve the design of the SGCG and the positioning of HCWs in the room.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".