Risk factors for SARS-CoV-2 infection and epidemiological profile of Brazilian anesthesiologists during the COVID-19 pandemic: cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: The devasting effects of COVID-19 have caused economic and health impacts worldwide. Anesthesiologists were one of the key professionals fighting the pandemic and have been highly exposed at their multiple sites of clinical practice. Thus, the importance of determining the nature of the infection in this population that provides care to SARS-CoV-2 patients. METHOD: We conducted a cross-sectional study administering an online questionnaire to examine the demographic and epidemiological profile of these professionals in Brazil, and to describe the risk factors for viral infection during the pandemic. RESULTS: A total of 1,127 anesthesiologists answered the questionnaire, 55.2% were men, more than 90% with age below 60 years, with infection and reinfection rates of 14.7% and 0.5%, respectively, and 47.2% reported a significant income reduction. The predictors of COVID-19 contamination were practicing in operating rooms (OR = 0.42; 95% CI 0.23-0.78), direct contact with infected patients (OR = 5.74; 95% CI 3.05-11.57), indirect contact with infected patients (OR = 2.43; 95% CI 1.13-5.33), working in a pre-hospital setting (OR = 2.36; 95% CI 1.04-5.03), and presence of immunosuppression, except for cancer (OR = 4.89; 95% CI 1.16-19.01). CONCLUSION: COVID-19 had enormous consequences on Brazilian anesthesiologists regarding sociodemographic aspects and contamination rates (5.57 times higher than in the general population). These are alarming and unprecedented findings for this professional group, as they reveal the considerable risk of infection and its independent predictor variables.
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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 | 0.001 | 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) | 0.001 | 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".