Physiological Effects of Wearing N95 Respirator on Medical Staff During Prolong Work Hours in Covid-19 Departments
Bibliographic record
Abstract
Abstract BackgroundSince the emergence of coronavirus disease, health care professionals in high-risk environments are mandated to wear N95 respirators for prolonged periods. The effect of this prolonged use on cardio-respiratory variables and gas-exchange is poorly defined. The objective of the current study was to determine gas exchange abnormalities and physiological changes among healthcare workers during a 4-hour emergency department (ED) shift while wearing the N95 respirator.MethodsThis was a single-center prospective observational study that consisted of medical staff working at the Sheba Medical Center ED. Physiological effects and gas exchange variables were obtained under normal breathing conditions and after 4-hour shifts while continuously wearing an N95 respirator. Comparisons of paired measurements were performed using a non-parametric Wilcoxon matched-pairs signed-rank test.ResultsForty-one subjects were included in the study. Prolonged N95 respirator use was associated with a significant decline in plasma pH [7.35mmHg vs. 7.34mmHg, P=0.02], PvO2 [23.2 mmHg vs. 18.6 mmHg, P<0.001] and a concurrent increase in EtCO2 [32.5mmHg vs. 38.5mmHg, p<0.0001]. PvCO2 and bicarbonate levels did not differ. No significant change was observed for heart rate or oxygen saturation. ConclusionUsing an N95 respirator for prolonged periods by healthcare professionals may provoke changes in gas exchange. The clinical significance of these changes in terms of symptoms or longer-term health status is unknown and remains to be determined.
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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 | 0.000 |
| 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) | 0.002 | 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".