Identifying and Overcoming the Barriers to Virtual Electromyography Assessments: A Scoping Review
Bibliographic record
Abstract
Introduction: Electromyography (EMG) assessments have been conducted virtually more frequently in recent years, leading researchers to explore the barriers to EMG assessments in a telehealth setting and how to overcome them. Methods: A scoping review was conducted according to the methodology described by Arksey and O'Malley. A comprehensive search using controlled vocabulary and keywords for two concepts, EMG and telehealth, was conducted using Medline and EMBASE on February 7, 2022. Two independent reviewers screened titles, abstracts, and full-text articles. Two reviewers also extracted the data and described the findings in a descriptive analysis. Results: A total of 248 articles were screened during the abstract and title review, of which 64 full texts were screened for eligibility. Of these, 15 publications met the inclusion criteria. Most articles were published in 2018 or later (66.7%). The most frequently mentioned barrier to conducting a virtual EMG assessment was poor data and signal transmission (53.3%). Another frequently mentioned barrier was poor patient usability (33.3%). Solutions most frequently reported related to patient usability (33.3%). These included interactive instructions and video chat to monitor and provide the patient with technical support. Conclusion: The last 4 years have seen an increase in articles published on EMGs' use in telehealth to monitor or diagnose patients. Further research is required to determine if the proposed solutions have improved clinical outcomes for the patient.
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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 | 0.050 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".