Radical Overlapping Intravelar Veloplasty during Primary Cleft Palate Repair Results in Decreased Secondary Speech Surgery
Bibliographic record
Abstract
BACKGROUND: There is growing evidence that performing a radical intravelar veloplasty (IVV) improves speech outcomes. The aim of this study was to determine the impact of a radical IVV during primary palatoplasty on the rate of secondary speech surgery. METHODS: This study was a retrospective review of primary palatoplasty using an IVV performed by a single surgeon from the years 2000 to 2023. In 2008, the surgeon changed technique to involve a more radical IVV. The radical overlapping IVV involves release of the palatopharyngeus from the posterior hard palate and from the lateral tendinous insertion of the tensor veli palatini, release of the levator veli palatini to the levator tunnel, and overlapping of the palatopharyngeus-levator unit across the midline after retropositioning. This separated the patients into a before and after technique change group. The rate of secondary speech surgery was compared between the 2 periods. RESULTS: An IVV was performed during straight line repairs 333 and 272 times during the first and second periods, respectively. The second radical overlapping IVV group had significantly ( P < 0.05) fewer secondary speech surgery procedures at 43 (15.81%) compared with 83 (24.92%) among the first conservative IVV group ( P < 0.05). CONCLUSION: Precise anatomical dissection, extensive release, retropositioning, and overlap of the velar musculature during IVV results in significantly fewer secondary speech surgical procedures. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.
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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 | 0.003 |
| 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) | 0.002 | 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".