Modified Von Langenbeck Cleft Palate Repair Using an Anterior Triangular Flap: Decreased Incidence of Anterior Oronasal Fistulas
Bibliographic record
Abstract
OBJECTIVE: A complication following cleft palate surgery is the development of oronasal fistulas. Despite recent advances aimed at addressing this concern, rates of postoperative fistulas have remained unchanged and are reported at between 3% and 60%. Oronasal fistulas commonly occur between the hard and soft palate and at the anterior portion of the cleft. These fistulas lead to functional problems with nasal emission, hypernasal speech, and food regurgitation through the nose. For clefts of the secondary palate, we developed a modification of the Von Langenbeck technique in which an anterior triangular flap is used to decrease the incidence of postoperative fistulas. METHOD: A triangular flap composed of oromucosa was designed for isolated clefts of the secondary palate only. It is based at the anterior margin of the cleft and is used as a turnover flap to allow closure of the often very tight anterior nasal side. A retrospective chart analysis was performed from 2000 to 2007. All patients who had isolated clefts of the secondary palate and had undergone a modified Von Langenbeck procedure were included in the study. Patients were evaluated 4 to 8 weeks postoperatively for the presence of oronasal fistulas. RESULTS: With the introduction of the anterior triangular flap, we show that 0 of 182 patients developed a postoperative oronasal fistula. CONCLUSIONS: This modification of the standard Von Langenbeck uses an anterior triangular flap and confers the advantage of assisting in nasal side closure of the anterior margin of the cleft; in doing so, it reduces the rate of fistula formation.
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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 | 0.000 | 0.002 |
| 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".