Telemedicine Use for Movement Disorders: A Global Survey
Bibliographic record
Abstract
BACKGROUND: Telemedicine is increasingly used to care for patients with movement disorders, but data regarding its global use are limited. INTRODUCTION: To obtain baseline international data about telemedicine use among movement disorder clinicians. METHODS: An online survey was sent to all 6,056 Movement Disorder Society members in 2015. Scope, reimbursement, and perceived quality of telemedicine were assessed. RESULTS: There were 549 respondents (9.1% overall response rate) from 83 countries. Most (85.8%) were physicians, and most (70.9%) worked in an academic or university practice. Half of respondents (n = 287, from 57 countries) used telemedicine for clinical care; activities included e-mail (63.2%), video visits (follow-up [39.7%] and new [35.2%]), and video-based education (35.2%). One hundred five respondents personally conducted video visits, most frequently to outpatient clinics (53.5%), patient homes (30.8%), and hospital inpatients (30.3%). The most common challenges were a limited neurological examination (58.9%) and technological difficulties (53.3%), and the most common benefits were reduced travel time (92.9%) and patient costs (60.1%). The most frequent reimbursements were none (39.0%), public insurance (24.5%), and patient payment (9.3%). Half of respondents planned to use telemedicine in the future, and three-quarters were interested in telemedicine education. CONCLUSIONS: More than 250 respondents around the world engage in telemedicine for movement disorders; most perceived benefit for patients, despite challenges and reimbursement for clinicians. Formal instruction on telemedicine is highly desired. Although the survey response was low and possibly biased to over represent those with telemedicine experience, the study provides baseline data for future comparison and to improve telemedicine delivery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".