Medical masks vs N95 respirators for preventing COVID‐19 in healthcare workers: A systematic review and meta‐analysis of randomized trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Respiratory protective devices are critical in protecting against infection in healthcare workers at high risk of novel 2019 coronavirus disease (COVID‐19); however, recommendations are conflicting and epidemiological data on their relative effectiveness against COVID‐19 are limited. Purpose To compare medical masks to N95 respirators in preventing laboratory‐confirmed viral infection and respiratory illness including coronavirus specifically in healthcare workers. Data Sources MEDLINE, Embase, and CENTRAL from January 1, 2014, to March 9, 2020. Update of published search conducted from January 1, 1990, to December 9, 2014. Study Selection Randomized controlled trials (RCTs) comparing the protective effect of medical masks to N95 respirators in healthcare workers. Data Extraction Reviewer pair independently screened, extracted data, and assessed risk of bias and the certainty of the evidence. Data Synthesis Four RCTs were meta‐analyzed adjusting for clustering. Compared with N95 respirators; the use of medical masks did not increase laboratory‐confirmed viral (including coronaviruses) respiratory infection (OR 1.06; 95% CI 0.90‐1.25; I 2 = 0%; low certainty in the evidence) or clinical respiratory illness (OR 1.49; 95% CI: 0.98‐2.28; I 2 = 78%; very low certainty in the evidence). Only one trial evaluated coronaviruses separately and found no difference between the two groups ( P = .49). Limitations Indirectness and imprecision of available evidence. Conclusions Low certainty evidence suggests that medical masks and N95 respirators offer similar protection against viral respiratory infection including coronavirus in healthcare workers during non–aerosol‐generating care. Preservation of N95 respirators for high‐risk, aerosol‐generating procedures in this pandemic should be considered when in short supply.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.023 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it