Risk of transmission of respiratory viruses during aerosol-generating medical procedures (AGMPs) revisited in the COVID-19 pandemic: a systematic review
Bibliographic record
Abstract
Abstract Background In many jurisdictions healthcare workers (HCWs) are using respirators for aerosol-generating medical procedures (AGMPs) performed on adult and pediatric populations with all suspect/confirmed viral respiratory infections (VRIs). This systematic review assessed the risk of VRIs to HCWs in the presence of AGMPs, the role respirators versus medical/surgical masks have on reducing that risk, and if the risk to HCWs during AGMPs differed when caring for adult or pediatric patient populations. Main text We searched MEDLINE, EMBASE, Cochrane Central, Cochrane SR, CINAHL, COVID-19 specific resources, and MedRxiv for English and French articles from database inception to September 9, 2021. Independent reviewers screened abstracts using pre-defined criteria, reviewed full-text articles, selected relevant studies, abstracted data, and conducted quality assessments of all studies using the ROBINS-I risk of bias tool. Disagreements were resolved by consensus. Thirty-eight studies were included; 23 studies on COVID-19, 10 on SARS, and 5 on MERS/ influenza/other respiratory viruses. Two of the 16 studies which assessed associations found that HCWs were 1.7 to 2.5 times more likely to contract COVID-19 after exposure to AGMPs vs. not exposed to AGMPs. Eight studies reported statistically significant associations for nine specific AGMPs and transmission of SARS to HCWS. Intubation was consistently associated with an increased risk of SARS. HCWs were more likely (OR 2.05, 95% CI 1.2–3.4) to contract human coronaviruses when exposed to an AGMP in one study. There were no reported associations between AGMP exposure and transmission of influenza or in a single study on MERS. There was limited evidence supporting the use of a respirator over a medical/surgical mask during an AGMP to reduce the risk of viral transmission. One study described outcomes of HCWs exposed to a pediatric patient during intubation. Conclusion Exposure to an AGMP may increase the risk of transmission of COVID-19, SARS, and human coronaviruses to HCWs, however the evidence base is heterogenous and prone to confounding, particularly related to COVID-19. There continues to be a significant research gap in the epidemiology of the risk of VRIs among HCWs during AGMPs, particularly for pediatric patients. Further evidence is needed regarding what constitutes an AGMP.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".