Patient preference for virtual versus in‐person visits in neuromuscular clinical practice
Bibliographic record
Abstract
INTRODUCTION/AIMS: It is unknown if patients with neuromuscular diseases prefer in-person or virtual telemedicine visits. We studied patient opinions and preference on virtual versus in-person visits, and the factors influencing such preferences. METHODS: Telephone surveys, consisting of 11 questions, of patients from 10 neuromuscular centers were completed. RESULTS: Five hundred and twenty surveys were completed. Twenty-six percent of respondents preferred virtual visits, while 50% preferred in-person visits. Sixty-four percent reported physical interaction as "very important." For receiving a new diagnosis, 55% preferred in-person vs 35% reporting no preference. Forty percent were concerned about a lack of physical examination vs 20% who were concerned about evaluating vital signs. Eighty four percent reported virtual visits were sufficiently private. Sixty eight percent did not consider expenses a factor in their preference. Although 92% were comfortable with virtual communication technology, 55% preferred video communications, and 19% preferred phone calls. Visit preference was not significantly associated with gender, diagnosis, disease severity, or symptom management. Patients who were concerned about a lack of physical exam or assessment of vitals had significantly higher odds of selecting in-person visits than no preference. DISCUSSION: Although neither technology, privacy, nor finance burdened patients in our study, more patients preferred in-person visits than virtual visits and 40% were concerned about a lack of physical examination. Interactions that occur with in-person encounters had high importance for patients, reflecting differences in the perception of the patient-physician relationship between virtual and in-person visits.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".