Technical Challenges for Laryngeal Electromyography
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Laryngeal electromyography (LEMG) is a useful diagnostic test in the evaluation of vocal fold paralysis (VFP). This study investigates factors that can make LEMG challenging to perform. METHODS: Patients with subacute unilateral VFP presented for LEMG were prospectively enrolled. Demographic data including BMI, previous neck surgery, and anatomic factors were collected. Patient-reported pain related to the procedure was recorded on a visual analogue scale (VAS). Electromyographer and otolaryngologist recorded a consensus rating of the perceived difficulty in performing the test and confidence in using the results for clinical decision-making. RESULTS: A total of 111 patients (56.8% female) were enrolled between August 2015 and August 2018. The mean age was 55 ± 14 years, and the average body mass index (BMI) was 28.5 ± 6.4. The mean patient-reported VAS score for pain was 35 ± 24. Notably, 31.2% of the tests were considered "very easy," 32.1% were considered "mildly challenging" and 23.9% and 12.8% were considered "moderately challenging" and "extremely challenging," respectively, by the clinicians. Common factors affecting LEMG difficulty included poorly palpable surface anatomy (50.5%) and patient intolerance (15.6%). Clinicians felt confident in 76.1% of the test findings. Bivariate analyses showed that prior neck surgery is associated with elevated VAS (p = 0.02), but clinician-perceived difficulty of performing the test is not associated with elevated VAS scores (p = 0.55). CONCLUSIONS: Majority of LEMG tests are well tolerated by patients. Physicians reported more confidence using LEMG for clinical decision-making when the test was easier to perform. Difficult surface anatomy and patient intolerance affects clinician confidence in integrating the test results with clinical care. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:831-834, 2024.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".