Laryngeal Electromyography-Guided Hyaluronic Acid Vocal Fold Injections for Glottic Insufficiency
Bibliographic record
Abstract
Objectives: To assess voice outcomes using the novel technique of in-office laryngeal electromyography-guided vocal fold injections (LEVFI) with hyaluronic acid to treat glottal insufficiency. Secondary objectives included determining the complication/completion rates and if any factors were associated with improved voice outcomes. Methods: Retrospective review of patients who received their first LEVFI from August 2017 to December 2018. Three- and six-month voice outcomes were assessed. Outcomes included voice handicap index-10 (VHI-10), maximum phonation time (MPT), perceptual analysis of voice (GRBAS), fundamental frequency, and stroboscopy. Results: Of the 121 eligible patients (55.4% male, age 63.7 years), 94 (77.7%) had complete 3-month data and 59 (48.8%) had complete 6-month data. VHI-10 was significantly improved from 25.7 ± 7.5 to 20.9 ± 10.9 at 3 months ( P < .001) and to 19.1 ± 11.5 at 6 months ( P < .001). MPT improved from 6.2 ± 5.4 seconds to 9.4 ± 7.1 seconds at 3 months ( P < .001) and to 11.3 ± 8.2 seconds at 6 months ( P < .001). GRBAS was improved in 74.8% of patients ([65.2, 82.8] 95% CI) at 3 months and 80.8% ([69.9, 89.1]) 95% CI) at 6 months. Stroboscopy showed a glottic gap improvement in 74.8% of patients ([65.8, 82.4] 95% CI) at 3 months and in 80.3% ([65.9, 88.5] 95% CI) at 6 months. Fundamental frequency was unchanged, as expected. Multivariate analysis reported that no factors were associated with better voice outcomes. Overall, 177/181 (97.8%) injections were completed. There were no complications. Conclusion: In-office LEVFI is an effective, novel technique to treat glottic insufficiency with improved voice outcomes, high completion rate, and no significant complications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".