Virtual Care in Rhinology
Bibliographic record
Abstract
BACKGROUND: The SARS-CoV-2 (COVID) pandemic has resulted in an increase in virtual care. While some specialties are well suited to virtual care, Otolaryngology - Head and Neck Surgery could be limited due to reliance on physical examination and nasal endoscopy, including Rhinology. It is likely virtual care will remain integrated for the foreseeable future and it is important to determine the strengths and weaknesses of this treatment modality for rhinology. METHODS: A survey on virtual care in rhinology was distributed to 61 Canadian rhinologists. The primary objective was to determine how virtual care compared to in-person care in each area of a typical appointment. Other areas focused on platforms used to deliver virtual care and which patients could be appropriately assessed by virtual visits. RESULTS: 43 participants responded (response rate 70.5%). The majority of participants use the telephone as their primary platform. History taking and reviewing results (lab work, imaging) were reported to be equivalent in virtual care. Non-urgent follow up and new patients were thought to be the most appropriate for virtual care. The inability to perform exams and nasal endoscopy were reported to be significant limitations. CONCLUSION: It is important to understand the strengths and limitations of virtual care. These results identify the perceived strengths and weaknesses of virtual care in rhinology, and will help rhinologists understand the role of virtual care in their practices.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".