Transition to virtual appointments for interventional neuroradiology due to the COVID-19 pandemic: a survey of satisfaction
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has changed the way medicine is practiced, including the implementation of virtual care in many specialties. In the field of interventional neuroradiology (INR), virtual clinics are an uncommon practice with minimal literature to support its use. Our objective was to report prospective, single-centre data regarding patient and physician experience with virtual INR clinics for routine follow-up appointments. METHODS: We surveyed all patients that participated in a virtual INR clinic follow-up appointment at our hospital over a 3 month period. Information gathered included length of appointment delays (ie, wait times), length of appointment times, overall satisfaction, and perceived safety metrics. A survey was also sent out to all physicians who participated in virtual clinics with similar questions. RESULTS: 118/122 patients and 6/6 physicians completed the survey. Wait times before previous in-person appointments were perceived to be much longer than virtual appointments, whereas in-person appointment times were longer. 112/118 (94.9%) patients and 4/6 (67%) physicians reported general satisfaction with their virtual clinic experience. There were 8/118 patients who felt their conditions could not be safely assessed virtually, compared with 1/6 (17%) physicians. Lastly, 72.2% of patients reported that they would prefer virtual or telephone visits in the future for non-urgent follow-up, and 5/6 (83%) of physicians reported the same. CONCLUSION: Virtual INR clinics are more efficient and are preferred among patients and physicians for non-urgent follow-up appointments. Our study demonstrates the feasibility of a virtual platform for INR care, which could be sustainable for future practice.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
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 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".