Virtual neurology survey: Factors influencing virtual care use among Ontario neurologists
Bibliographic record
Abstract
Background and objectives: Current virtual care guidance lacks specialty-specific considerations. Neurological care is unique due to its reliance on physical examination and complex patient population. Our aim was to determine which factors impact virtual care suitability in neurology, virtual care adoption patterns, and satisfaction with virtual care among neurologists. Methods: Surveys were sent to Ontario neurologists through a shared email from September to November 2021. The survey consisted of four parts: demographics, virtual care adoption patterns, factors influencing virtual care use, and physician satisfaction with virtual care. Results: Sixty-six of 380 (17.4%) neurologists completed the survey. The pandemic resulted in a substantial increase in virtual care use, from 1.6% of all ambulatory visits in 2019 to 70.6% in 2020. Video teleconferencing was considered more appropriate across a broader range of presentations than phone visits, with both methods more suited to follow-ups. Neurologists were largely satisfied with virtual care except for the virtual neurological examination. The neurological presentations identified as least amenable to virtual consultation were movement disorders, limb weakness, gait/balance changes, and vision changes. Four presentations were felt to be most amenable to virtual care: sleep disorders, seizure, headache, and dizziness/syncope. Factors that were felt to reduce virtual care suitability included discussion of sensitive topics and acute presentations. Conclusion: Neurologists were satisfied with virtual care as a means of providing outpatient care, though the specific reason for referral influenced perceived appropriateness. These results can inform the basis of the development of consensus guidelines for virtual care provision in neurology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".