Principles and Techniques to Reduce the Incidence of Fistula: A Review of over 1000 Cleft Palate Repairs by a Single Surgeon
Bibliographic record
Abstract
BACKGROUND: Fistula is a significant complication following cleft palate repair. This study presents the lowest incidence of postoperative fistula in a patient cohort of its size. The series provides an opportunity to describe the techniques and principles that the authors believe are important to reduce the rate of fistulas. METHODS: This is a retrospective review of prospectively collected data of 1041 consecutive cleft palate repairs performed by a single surgeon. The number of postoperative fistulas (defined as any oronasal communication excluding any residual intentionally unrepaired alveolar clefts) was determined. Regression analysis was performed to determine the factors that impact the risk of developing a fistula. RESULTS: Nine patients developed a postoperative fistula, representing an incidence of 0.86%. All patients who developed a fistula had a straight-line repair ( n = 882). None of the patients who underwent a Furlow palatoplasty developed a fistula ( n = 159). Veau type (OR; 2.88, P = 0.04) and the use of buccal myomucosal flaps (OR, 19; P = 0.02) were associated with a risk of developing a fistula. Five of the 9 patients who developed a fistula had a Veau group IV cleft. CONCLUSIONS: This study presents a large series with a fistula rate that compares favorably to the published literature. The authors present the principles and techniques believed to reduce postoperative fistula rates: appropriate preoperative management, respect for tissue, minimizing tension, and careful postoperative management. Detailed surgical videos are presented.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".