Physicians Preferences of Virtual Versus In-Person Visits in Neuromuscular Clinical Practice
Bibliographic record
Abstract
Background: 
 While the role of telemedicine is well established in certain fields of medicine, its role in disciplines like Neuromuscular medicine is not clear. COVID 19 pandemic compelled the medical community to utilize telemedicine and policies were rapidly changed to continue patient care during the pandemic. However, to guide the future of telemedicine in this field where a physical exam is an integral part of the visit, it is imperative to get a physician's opinion on this matter. We designed this study to assess the opinion of neuromuscular physicians about telemedicine, their preference, and factors influencing their decision.
 Methods:
 We used an online form composed of eleven questions to survey 94 neuromuscular specialists across the USA and Canada during September 2020.
 Results:
 90.43% of participating neuromuscular specialists preferred physical visits with new patients versus 44.68% preferred physical visits with follow-up patients. The majority thought that telemedicine reduces revenue (58.51%), quality of service (57.45%), and quality time spent with patients (62.77%). Nevertheless, most surveyed physicians agreed that telemedicine is time-efficient (84.04%), improves patient compliance (70.21%), and will be a long-term solution in clinical practice (67.02%). Finally, 58.51% revealed that telemedicine does not affect workload.
 Conclusion:
 Neuromuscular specialists preferred seeing new patients and revealing a new diagnosis to the patient in physical visits, but they also considered telemedicine a long-term method that would continue to increase in the post-pandemic future, emphasizing the need to address their concerns to facilitate telemedicine. 
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".