Quantitative laryngeal electromyography parameters may correlate with improved outcomes following botulinum toxin injection for spasmodic dysphonia
Bibliographic record
Abstract
BACKGROUND: Despite use of qualitative laryngeal electromyography (LEMG) guided botulinum toxin A (BoNT-A) injection for treatment of adductor spasmodic dysphonia (AdSD), unsatisfactory injections and complete "misses" remain problematic. We aimed to determine if the quantitative LEMG measure of number of small segments (NSS) correlates with voice outcomes following (BoNT-A injection for AdSD. METHODS: Automated quantitative LEMG analysis was performed during electromyography (EMG) -guided BoNT-A injection into the thyroarytenoid-lateral cricoarytenoid muscle complex for treatment of AdSD. Pre-injection phonatory NSS values were correlated with clinical voice outcomes and patient reported injection results. RESULTS: Quantitative LEMG measures were obtained for 45 AdSD patients (28 female, mean age 60.8 ± 12.8 years) during EMG-guided BoNT-A injection. Mean sampled NSS during phonation immediately prior to BoNT-A injection was 524 ± 323 (range: 2-904). Mean follow up was 36.5 ± 9.4 days; one patient was lost to follow-up. In comparison to their previous BoNT-A injection, the current injection was rated as worse, same, and better by 13 (29.5%), 25 (56.8%), and 6 (13.6%) patients, respectively. All 4 (9.1%) patients with NSS < 200 rated their BoNT-A injection result as worse than previous, and change in Voice Handicap Index-10 (VHI-10) scores were worse or without change. CONCLUSIONS: Aiming for an NSS value greater than 200 during phonation prior to BoNT-A toxin injection for AdSD may reduce unfavorable voice outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".