Virtual <scp>EMG</scp>: Safe and Effective Remote Supervision and Reporting
Bibliographic record
Abstract
The COVID-19 pandemic accelerated the implementation of virtual care in healthcare, revealing challenges and opportunities in the use of remote healthcare services. Electromyography (EMG) traditionally requires in-person interaction, limiting access for patients in remote or underserved areas. This study explores the feasibility of performing needle EMG remotely, a concept referred to as "virtual EMG," whereby an experienced clinician supervises a technician from a different location using virtual tools. We developed a system utilizing remote video technology, a high-resolution camera, and Bluetooth communication devices to allow real-time observation and guidance of EMG procedures. The system was tested across three phases: (1) initial setup within the same facility, (2) refinement with higher quality video and communication tools, and (3) application in a clinical scenario involving a complex patient case. Our findings suggest that virtual EMG can be performed safely and effectively, providing accurate diagnostic information while reducing the need for patient travel and in-person contact. The implementation of virtual EMG has the potential to improve access to neuromuscular diagnostics, especially in rural and remote areas, aligning with the healthcare goals of enhancing patient experience, reducing costs, and promoting access. Further investigations are needed to validate this approach across various clinical scenarios and geographical locations.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".