International registry of otolaryngologist–head and neck surgeons with COVID‐19
Bibliographic record
Abstract
BACKGROUND: It has become clear that healthcare workers are at high risk, and otolaryngology has been theorized to be among the highest risk specialties for coronavirus disease 2019 (COVID-19). The purpose of this study was to detail the international impact of COVID-19 among otolaryngologists, and to identify instructional cases. METHODS: Country representatives of the Young Otolaryngologists-International Federation of Otolaryngologic Societies (YO-IFOS) surveyed otolaryngologists through various channels. Nationwide surveys were distributed in 19 countries. The gray literature and social media channels were searched to identify reported deaths of otolaryngologists from COVID-19. RESULTS: A total of 361 otolaryngologists were identified to have had COVID-19, and data for 325 surgeons was available for analysis. The age range was 25 to 84 years, with one-half under the age of 44 years. There were 24 deaths in the study period, with 83% over age 55 years. Source of infection was likely clinical activity in 175 (54%) cases. Prolonged exposure to a colleague was the source for 37 (11%) surgeons. Six instructional cases were identified where infections occurred during the performance of aerosol-generating operations (tracheostomy, mastoidectomy, epistaxis control, dacryocystorhinostomy, and translabyrinthine resection). In 3 of these cases, multiple operating room attendees were infected, and in 2, the surgeon succumbed to complications of COVID-19. CONCLUSION: The etiology of reported cases within the otolaryngology community appear to stem equally from clinical activity and community spread. Multiple procedures performed by otolaryngologists are aerosol-generating procedures (AGPs) and great care should be taken to protect the surgical team before, during, and after these operations.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".