Functional soft palate reconstruction: A comprehensive surgical approach
Bibliographic record
Abstract
BACKGROUND: Dysfunction of the soft palate is devastating to the patient's quality of life, resulting in unintelligible speech and poor swallowing. Reconstruction of the soft palate is complex because the dynamic fibromuscular structure cannot be duplicated. The efficacy of soft palate reconstruction has therefore been called into question. The purpose of this article is: (1) to describe our comprehensive surgical paradigm for soft palate reconstruction, (2) to provide details of the surgical techniques used, and (3) to report on patient functional outcomes. METHODS: Fifty-two patients spanning 3 different size-based categories of soft palate reconstruction were included in the final analysis. Using videofluoroscopic studies of swallowing, the presence of nasopharyngeal reflux and any instance of aspiration of a bolus into the airway was noted. In addition, a simple diet survey was completed, and the use of a g-tube was noted. RESULTS: The results revealed that our protocol for soft palate reconstruction provided the majority of our patients with separation of the oropharynx and nasopharynx, while maintaining nasal patency. Restoration of swallowing function was timely, with 91% of the patients returning to an oral diet at the early postoperative visit and only 14% of patients demonstrating mild nasopharyngeal reflux. CONCLUSION: We have developed a comprehensive reconstructive protocol that provides patients with separation of the oropharynx and nasopharynx, while maintaining nasal patency. Restoration of function is timely, with reestablishment of normal intelligibility and resonance of speech as well as safe and efficient swallowing function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".