Visual biofeedback for paradoxical vocal fold motion (PVFM)
Bibliographic record
Abstract
OBJECTIVES: Paradoxical vocal fold motion (PVFM) is a common condition where the vocal folds inappropriately adduct during inspiration. This results in dyspnea and occasionally significant distress. The condition is thought to be primarily functional, with behavioural therapy considered mainstay in the non-acute setting. However, practice variations and limited access to speech language pathology (SLP) services can pose management challenges. We aimed to examine the efficacy of surgeon performed visual biofeedback as first-line treatment for PVFM. STUDY DESIGN: Prospective, non-randomized, non-comparative clinical study. METHODS: Adult patients referred for possible PVFM and congruent laryngoscopy findings over a two-year period were included. Patients were excluded if they presented in acute distress, had alternate diagnosis to explain symptomology and/or coexisting untreated lower respiratory pathology. Patients underwent immediate surgeon-performed visual biofeedback on the same visit day. The primary outcome of interest was change in Dyspnea Index (DI) scores pre- and post-intervention 3 months follow-up. The secondary outcome measured was change in asthma medication use from baseline to follow-up. RESULTS: Of 34 patients presenting, 25 met inclusion criteria. Of these, 72% were female with an average age of 36.9 ± 14.1. Approximately 48% of patients had a diagnosis of well-controlled asthma at presentation and co-morbid psychiatric diagnoses were common (52%). Pre- and post-intervention analysis showed significant improvement in DI scores (p < 0.001) and reduction in bronchodilator use (p = 0.003). CONCLUSION: This is a prospective study that evaluates the role of visual biofeedback in PVFM patients. Our data suggests that visual biofeedback effectively reduces short-term subjective symptoms and asthma medication use. LEVEL OF EVIDENCE: 3.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".