Factors That Limit Evaluation of Velopharyngeal Closure During Nasopharyngoscopy
Bibliographic record
Abstract
Objective To assess the quality of nasopharyngoscopy video recordings used for velopharyngeal insufficiency (VPI) surgical planning and identify factors that limit evaluation of velopharyngeal closure. Design Prospective observational study. Setting Metropolitan-based hospitals with craniofacial clinics in the United States and Canada. Participants One-hundred and forty-two (142) patients with VPI across 10 hospitals. Assessment(s) Nasopharyngoscopy video recordings used for VPI surgical planning . Main Outcome Measure(s) Ratability of nasopharyngoscopy video recordings, with “ratable” defined as the video (1) visualized the velum, lateral pharyngeal wall, and posterior pharyngeal walls at some point during speech production and (2) contained an oral speech sample at the phrase level or above. Results One-hundred and forty-two (142) nasopharyngoscopy video recordings were obtained from patients undergoing VPI evaluation, of which 59.9% (n=85) were ratable. A multilevel logistic regression model was used to identify factors that influenced the quality of nasopharyngoscopy video recordings. Factors associated with unratable nasopharyngoscopy videos were age ( P =.030), sex ( P =.005*), type of scope camera used ( P =.039), presence of compensatory misarticulations ( P =.008), and a limited speech sample ( P =.040). Conclusions A substantial proportion of nasopharyngoscopy video recordings obtained during VPI evaluation are not sufficient for rating velopharyngeal closure. Lack of ratability could impact the surgery selected to treat VPI. Younger patients, those with limited speech samples, or patients with extensive compensatory articulations may be more successful in completing other VPI imaging techniques, such as videofluoroscopy or magnetic resonance imaging.
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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 | 0.005 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".