N95 Filtering Facepiece Respirator Use during Pregnancy: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: This study was aimed to systematically review the use of filtering facepiece respirators, such asN95 masks, during pregnancy. STUDY DESIGN: A comprehensive search for primary literature using Medline, Embase, Scopus, Web of Science, and ClinicalTrials.gov was conducted from inception until April 2020 to find articles reporting outcomes of pregnant women using filtering facepiece respirator (FFR). Studies were selected if they included the use of FFR in pregnant women and reported an outcome of interest including physiologic changes (heart rate, respiratory rate, pulse oximetry, and fetal heart rate tracing) or subjective measures (thermal or exertional discomfort or fit). The Newcastle-Ottawa Quality Assessment scale was used to assess the risk of bias. The main outcome was to describe the physiologic changes in pregnant women compared with nonpregnant women. Due to the small number of studies and heterogeneity of reported outcomes a meta-analysis was not conducted. Results of the studies were synthesized into a summary of evidence table. RESULTS: We identified four studies, three cohort studies and one crossover study, comprising 42 women using FFR during pregnancy. Risk of bias was judged to be low. Studies were consistent in showing no significant increase in maternal heart rate, respiratory rate, oxygen saturation, and fetal heart rate between pregnant and nonpregnant women using N95 FFRs for short durations. Repeat fit testing was not supported for women gaining the recommended amount of weight during pregnancy. No evidence was found to reach conclusions about prolonged N95 FFR use in pregnancy. CONCLUSION: Limited duration N95 FFR use during pregnancy is unlikely to impart risk to the pregnant women or her fetus. KEY POINTS: · Limited N95 use unlikely to impart risk to pregnant woman/fetus.. · Prolonged N95 use in pregnancy is unstudied.. · Repeat fit testing in pregnancy likely unnecessary..
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".