The Value of Dynamic Voice CT Scan for Complex Airway Patients Undergoing Voice Surgery
Bibliographic record
Abstract
Introduction: Dynamic voice computerized tomography (DVCT) is a novel technique that provides additional information to characterize laryngeal function for patients with complex airway history that may alter surgical decisions. The goal of this study was to evaluate the impact of DVCT on decision making for reconstructive voice surgery for a cohort of post-airway reconstruction dysphonia patients. Methods: Retrospective chart review at a pediatric tertiary care center for patients with history of complex airway surgery and subsequent reconstructive voice surgery for dysphonia between 2010 and 2016. The study group had a DVCT prior to surgery while the control group underwent surgery without a DVCT. Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V) and pediatric Voice Handicap Index (pVHI) scores were evaluated by the voice clinic team (otolaryngologist, speech therapist) before and after voice surgery. Results: Twenty-one patients were analyzed (14 female, 67%) with a mean age (SD) of 14 (4.5) years old. Ninety percent (17/21) had a prior tracheostomy and a mean (SD) of 2.6 (1.3) airway surgeries. Twelve patients (57%) underwent DVCT prior to reconstructive voice surgery. CAPE-V baseline scores were similar between study and controls (means [SE] = 49 [4.6] and 57 [6.0], P = .72). However, scores significantly improved for the study group after voice surgery (mean [SE] = 31 (4.7), P < .0001) while controls did not improve (58 [5.7], P = .99). Baseline VHI scores were similar between both groups: mean (SE) = 54 (5.4) versus 52 (6.2), respectively, P = .99. Postsurgically, VHI scores were also similar between both groups (means [SE]: 46 [7.1] vs 47 [4.5], P = .99). Reconstructive voice surgery for study patients included posterior cricoid reduction (46%), vocal fold medialization/augmentation (46%), and laryngeal reinnervation (7.7%) while all controls underwent a single treatment (vocal fold medialization/augmentation). Conclusion: Patients with preoperative DVCT were more likely to have improvement. DVCT appeared to have altered surgical decision making and has allowed tailoring of reconstructive surgery to specific patients’ needs. DVCT could represent an important tool prior to reconstructive surgery to guide the choice of surgical procedures for complex airway patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".