Evaluation of real and perceived risk to health care workers caring for patients with the Omicron variant of the SARS-CoV-2 virus in surgery and obstetrics
Bibliographic record
Abstract
Abstract Introduction The Omicron variant of the SARS-CoV-2 virus is described as more contagious than previous variants. We sought to assess risk to healthcare workers (HCWs) caring for patients with COVID-19 in surgical/obstetrical settings, and the perception of risk amongst this group. Methods From January to April, 2022, reverse transcription polymerase chain reaction was used to detect the presence of SARS-CoV-2 viral RNA in patient, environmental (floor, equipment, passive air) samples, and HCW’s masks (inside surface) during urgent surgery or obstetrical delivery for patients with SARS-CoV-2 infection. The primary outcome was the proportion of HCWs’ masks testing positive. Results were compared with our previous cross-sectional study involving obstetrical/surgical patients with earlier variants (2020/21). HCWs completed a risk perception electronic questionnaire. Results 11 patients were included: 3 vaginal births and 8 surgeries. 5/108 samples (5%) tested positive (SARS-CoV-2 Omicron) viral RNA: 2/5 endotracheal tubes, 1/22 floor samples, 1/4 patient masks and 1 nasal probe. No samples from the HCWs masks (0/35), surgical equipment (0/10) and air samples (0/11) tested positive. No significant differences were found between the Omicron and 2020/21 patient groups’ positivity rates (Mann-Whitney U test, p = 0.838) or the level of viral load from the nasopharyngeal swabs (p = 0.405). Nurses had a higher risk perception than physicians (p = 0.038). Conclusion No significant difference in contamination rates were found between SARS-CoV-2 Omicron BA.1 and previous variants in surgical/obstetrical settings. This is reassuring as no HCW mask was positive and no HCW tested positive for COVID-19 post-exposure.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".