Prospective Outcomes After Serial Platelet‐Rich Plasma (<scp>PRP</scp>) Injection in Vocal Fold Scar and Sulcus
Bibliographic record
Abstract
OBJECTIVE: Vocal fold scar and sulcus pose significant treatment challenges with no current optimal treatment. Platelet-rich plasma (PRP), an autologous concentration of growth factors, holds promise for regenerating the superficial lamina propria. This study aims to evaluate the potential benefits of serial PRP injections on mucosal wave restoration and vocal function. METHODS: In a prospective clinical trial across two institutions, patients with vocal fold scar underwent four serial PRP injections, one month apart. Blinded independent laryngologists and expert listeners used pretreatment and one-month post-fourth injection videostroboscopy and CAPE-V assessments to evaluate mucosal wave and voice quality changes, respectively. Additionally, patient reported outcome measures (PROMs) were evaluated. RESULTS: In the study, 15 patients received 55 PRP injections without adverse effects. Eight patients (53.3%) had mild, three patients (20%) had moderate, and four patients (26.7%) had severe scar. There was an average reduction of 8.7 points in post-treatment VHI-10 scores (p = 0.007). The raters observed an improvement in post-treatment voice in 73.4% of cases, and CAPE-V scores showed a reduction of 18.8 points on average (p = 0.036). The videostroboscopic VALI ratings showed an improvement in mucosal wave rating from 2.0 to 4.0. On average, the raters perceived the post-PRP exams to be better in 56.7% of cases. CONCLUSIONS: PRP has been validated as a safe autologous option for treatment of vocal fold scar. While results for mucosal wave and voice quality varied, there was a consistent improvement in PROMs. LEVEL OF EVIDENCE: 3: Prospective cohort study, with blinded analysis Laryngoscope, 134:5021-5027, 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".