Ventilation Techniques and Risk for Transmission of Coronavirus Disease, Including COVID-19
Bibliographic record
Abstract
Background: \nMechanical ventilation is used to treat respiratory failure in coronavirus disease 2019 (COVID-19). \n \nPurpose: \nTo review multiple streams of evidence regarding the benefits and harms of ventilation techniques for coronavirus infections, including that causing COVID-19. (PROSPERO registration: CRD42020178187) \n \nData Sources: \n21 standard, World Health Organization–specific and COVID-19–specific databases, without language restrictions, until 1 May 2020. \n \nStudy Selection: \nStudies of any design and language comparing different oxygenation approaches in patients with coronavirus infections, including severe acute respiratory syndrome (SARS) or Middle East respiratory syndrome (MERS), or with hypoxemic respiratory failure. Animal, mechanistic, laboratory, and preclinical evidence was gathered regarding aerosol dispersion of coronavirus. Studies evaluating risk for virus transmission to health care workers from aerosol-generating procedures (AGPs) were included. \n \nData Extraction: \nIndependent and duplicate screening, data abstraction, and risk of bias assessment (GRADE for certainty of evidence and AMSTAR 2 for included systematic reviews). \n \nData Synthesis: \n123 studies were eligible (45 on COVID-19, 70 on SARS, 8 on MERS), but only 5 studies (1 on COVID-19, 3 on SARS, 1 on MERS) adjusted for important confounders. A study in hospitalized patients with COVID-19 reported slightly higher mortality with noninvasive ventilation (NIV) than with invasive mechanical ventilation (IMV), but 2 opposing studies, 1 in patients with MERS and 1 in patients with SARS, suggest a reduction in mortality with NIV (very low-certainty evidence). Two studies in patients with SARS report a reduction in mortality with NIV compared with no mechanical ventilation (low-certainty evidence). Two systematic reviews suggest a large reduction in mortality with NIV compared with conventional oxygen therapy. Other included studies suggest increased odds of transmission from AGPs. \n \nLimitation: \nDirect studies in COVID-19 are limited and poorly reported. \n \nConclusion: \nIndirect and low-certainty evidence suggests that use of NIV, similar to IMV, probably reduces mortality but may increase the risk for transmission of COVID-19 to health care workers.
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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 | 0.014 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".