Surgical Masks for Protection of Health Care Personnel Against Covid-19: Results from an Observational Study
Bibliographic record
Abstract
PURPOSE: The aim of the study was to describe the use of masks among health care personnel (HCP) exposed to index cases of coronavirus disease 2019 (COVID-19), and to evaluate any association with infection rate. METHODS: We did a retrospective, observational study of HCP at Zhongnan Hospital of Wuhan University for the management of COVID-19 (before person-to-person transmission was official confirmed, no additional protection was provided). A questionnaire was given to all staff listed on the roster in the clinical regions providing care for index patients with COVID-19. All participants were surveyed regarding hand-washing and use of surgical masks and gloves and were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Data were analysed (Student's t test and Pearson χ2 test) for an association between infection and use of personal protective equipment. RESULTS: Exposure of a total of 299 non-infected and 30 infected staff was confirmed. None of the 149 staff who reported use of all three preventative measures (hand-washing and use of gloves and masks) became infect-ed. In contrast, all 30 of the staff who became infected had omitted at least one of the measures. Fewer staff who wore surgical masks (P=0.000003) became infected compared with those who did not. Infections rates were significantly lower in HCP from the internal medicine departments, as these personnel generally wore masks. CONCLUSION: An association was found between SARS-CoV-2 infection of HCP and the non-use of masks when working with index cases in clinical settings. We recommend that all HCP follow the strict instructions for prevention and treatment of nosocomial infection during intimate contact with COVID-19, especially staff from surgical departments.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".