CSO (Canadian Society of Otolaryngology – Head & Neck Surgery) position paper on return to Otolaryngology – Head & Neck Surgery Clinic Practice during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
The novel Coronavirus (COVID-19) has created a worldwide deadly pandemic that has become a major public health challenge. All semi-urgent and elective medical care has come to a halt to conserve capacity to care for patients during this pandemic. As the numbers of COVID-19 cases decrease across Canada, our healthcare system also began to reopen various facilities and medical offices. The aim for this document is to compile the current evidence and provide expert consensus on the safe return to clinic practice in Otolaryngology - Head & Neck Surgery. These recommendations will also summarize general precaution principles and practical tips for office across Canada to optimize patient and provider safety. Risk assessment and patient selection are crucial to minimizing exposure to COVID-19. Controversial topics such as COVID-19 mode of transmission, duration of exposure, personal protective equipment, and aerosol-generating procedures will be analyzed and discussed. Practical solutions of pre-visit office preparation, front office and examination room set-up, and check out procedures are explored. Specific considerations for audiology, pediatric population, and high risk AGMPs are also addressed. Given that the literature surrounding COVID-19 is rapidly evolving, these guidelines will serve to start our specialty back into practice over the next weeks to months and they may change as we learn more about this disease.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".