One year of COVID 19: Results of a national web-based survey on the SARS-CoV-2 infectious state of otorhinolaryngologists in Germany
Bibliographic record
Abstract
Background Otorhinolaryngologists (ORL) are said to be at high risk due to a close professionally contact with the mucosa of the upper airway where SARS-CoV-2 can be detected to a high degree. Anyhow, only few data is available for German ORLs. Methods The German Society of ORL, Head and Neck Surgery and the German ENT Association addressed German ORLs to participate in a web-based survey about infection with SARS-CoV-2. Data of infections and concomitant parameters in German ORLs were compared to the total number of infections in Germany. An initial survey was launched May 2020 and a monthly follow-up survey was active until January 2021. Results 970 out of 6383 German ORLs (15 % ) participated in the initial survey and the June follow-up. Testing positive for SARS-CoV-2 until June 2020 was reported by 54 ORLs. The relative risk of contracting SARS-CoV-2 for ORLs is calculated as an OR of 3.67 (95 % CI 2.82; 4.79) compared to the total population of Germany. As treatment, 2 individuals were admitted to hospital without intensive care and domestic quarantine was conducted in 96.3 % of cases. No casualties were reported. In 31 cases the source of infection was not identifiable whereas 23 had a clear etiology: infected patients: n = 5, 9.26 % ; medical staff: n = 13, 24.1 % . 9.26 % (n = 5) of the identified cases were related to contact to infected family members (n = 3), closer neighborhood (n = 1) or general public (n = 1). There does not seem to be an increased risk of infection performing surgery. The follow-up data for 12 month of COVID-19 will be implemented in the presentation. Conclusion There is an almost 3.7-fold risk of contracting SARS-CoV-2 for German ORLs compared to the population baseline level. Appropriate protection appears to be necessary for this occupational group. Poster-PDF A-1017.pdf Publication History Article published online: 13 May 2021 © 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/). Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".