Do patients regret having in-office vocal fold injections for glottic insufficiency?
Bibliographic record
Abstract
BACKGROUND: In-office vocal fold injections (VFI) are an effective treatment for glottic insufficiency. The primary objective of this study was to assess if patients reported decisional regret after VFI. Secondary objectives included determining if variables were associated with lower decisional regret. METHODS: Case-control study of patients who underwent in-office VFIs for glottic insufficiency from August 2017 to December 2019 at a tertiary laryngology clinic. Participants completed the validated Decision Regret Scale (DRS). Demographic data, clinician's perceptual analysis with GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain), and patient's self-reported Voice Handicap Index-10 (VHI-10) were analyzed. Nonparametric tests as well as univariate and multiple logistics regression were performed. RESULTS: Of patients eligible, 75% (136/182) completed the DRS (mean age 65.4 years (SD 13.9), 58.1% male). Eighty-three (61.0%) reported no decisional regret, thirty-three (24.3%) reported mild decisional regret, and twenty (14.7%) reported moderate to strong decisional regret. Improvement in most recent VHI-10 (Kendall correlation coefficient tau = 0.156, p = 0.029), Grade of voice (tau = 0.236, p value = 0.002) and Breathiness of voice (tau = 0.150, p = 0.044) were associated with lower DRS. Multivariate logistics regression results showed that the change in Grade of voice (OR 9.9, p < 0.01), Roughness (OR 0.2, p < 0.01) and Breathiness (OR 0.2, p < 0.03) were significantly associated with DRS. CONCLUSION: The majority of patients had no or mild decisional regret after in-office VFI for glottic insufficiency. Both patients who reported less vocal handicap after VFI and clinician-noted improvements in perceptual evaluation of voice after VFI were associated with significantly lower decisional regret.
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 | 0.002 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".