Prospective >12 Months Outcomes After Vocal Fold Injection Medialization With Silk <scp>Microparticle‐Hyaluronic</scp> Acid Material
Bibliographic record
Abstract
OBJECTIVE: Vocal fold injection medialization (VFIM) is widely used as an initial treatment for unilateral vocal fold paralysis (UVFP). Current practices employ materials that share the limitation of temporary clinical effect from variable resorption rates. A novel silk protein microparticle-hyaluronic acid-based material (silk-HA) has demonstrated cellular infiltration and tissue deposition that may portend a durable medialization effect. We report on ≥12 months outcomes after VFIM with silk-HA. METHODS: Prospective open-label study of patients with UVFP that elected treatment with VFIM with silk-HA. Blinded experts rated laryngeal stroboscopic exams. RESULTS: Seventeen patients with UVFP underwent VFIM with silk-HA. Twelve of the 17 patients have ≥12 months follow-up. Seven patients demonstrated durable treatment benefit ≥12 months after injection with median improvement of 19 (p = 0.0156) in VHI-10. There was no significant change in VHI-10 between 1 and 12 months for these patients. Blinded ratings indicated that 5/7 patients with sustained improvements in VHI-10 exhibited complete or touch glottal closure at 12 months. Two of the seven patients exhibited a small (<1 mm) glottal gap at 12 months. Seven patients experienced initial benefit with later regression 3-4 months after injection. CONCLUSION: VFIM with silk-HA can offer durable improvement in voice-related outcomes for UVFP past 12 months. A subset of patients treated with silk-HA experienced early loss of effect around 3-4 months postinjection. Clinical factors predictive of sustained treatment response to silk-HA injection require further exploration. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:3679-3685, 2024.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".