Correction of the Cleft Lip Lateral Bulge Deformity Using Anatomic Muscle Repair
Bibliographic record
Abstract
BACKGROUND: The lateral bulge deformity may result after primary cleft lip repair. In a pilot study, greater orbicularis oris thickness and levator width underlying the lateral bulge were identified using ultrasound. The purpose of this study was to evaluate postoperative results of anatomic muscle repair for lateral bulge correction. METHODS: Patients with a lateral bulge after primary unilateral cleft lip repair were prospectively recruited. Oronasal musculature and connective tissue dimensions were measured using ultrasound, preoperatively and postoperatively. Guided by preoperative ultrasound findings in each patient, lateral bulge correction consisted of total lip takedown and anatomic orbicularis oris reapproximation. Within each group, measurements between sides at corresponding landmarks were compared using t-tests. Ratios between sides at corresponding landmarks preoperatively and postoperatively were compared using parametric and nonparametric tests. Repeat measurements were performed to calculate intrarater reliability. Standardized video assessments of dynamic lip function were recorded preoperatively and postoperatively. RESULTS: Average patient age was 17.4 years. Patients were evaluated preoperatively and postoperatively (n=14) at 7.8 months' mean follow-up. Cleft-side orbicularis thickness and levator width were greater preoperatively versus postoperatively (P=0.003 and P=0.018, respectively). Postoperatively, no differences were seen between sides for both orbicularis thickness (P=0.763) and levator width (P=0.626). All patients demonstrated improved lip contour and symmetry, both static and dynamically, on video assessments. CONCLUSIONS: Lip contour, function, and aesthetics improved clinically, and lip muscle anatomy normalized postoperatively as assessed using ultrasound. Complete orbicularis oris takedown and anatomic reapproximation effectively addressed the lateral bulge deformity.
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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 | 0.001 |
| 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".