Through-and-Through Dissection of the Soft Palate for Pharyngeal Flap Inset: A “Good-Fast-Cheap” Technique for Any Etiology of Velopharyngeal Incompetence
Bibliographic record
Abstract
OBJECTIVE: To determine the efficacy and resource utilization of through-and-through dissection of the soft palate for pharyngeal flap inset for velopharyngeal incompetence (VPI) of any indication. DESIGN: Retrospective review. SETTING: Tertiary care center. PATIENTS: Thirty patients were included. Inclusion criteria were diagnosis of severe VPI based on perceptual speech assessment, confirmed by nasoendoscopy or videofluoroscopy; VPI managed surgically with modified pharyngeal flap with through-and-through dissection of the soft palate; and minimum 6 months follow-up. Patients with 22q11.2 deletion syndrome were excluded. INTERVENTION: Modified pharyngeal flap with through-and-through dissection of the soft palate. MAIN OUTCOME MEASURE(S): Velopharyngeal competence and speech assessed using the Speech-Language Pathologist 3 scale. RESULTS: < .001). Velopharyngeal competence was restored in 25 (83%) patients, borderline competence in 3 (10%), and VPI persisted in 2 (7%) patients. Complications included 1 palatal fistula that required elective revision and 1 mild obstructive sleep apnea that did not require flap takedown. Median skin-to-skin operative time was 73.5 minutes, and median length of stay (LOS) was 50.3 hours. CONCLUSIONS: This technique allows direct visualization of flap placement and largely restores velopharyngeal competence irrespective of VPI etiology, with low complication rates. Short operative time and LOS extend the value proposition, making this technique not only efficacious but also a resource-efficient option for surgical management of severe VPI.
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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 | 0.003 |
| 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 | 0.000 | 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".