Secondary Cleft Rhinoplasty Using Circumferential Alar Rim Cartilage Graft: Surgical Technique and Morphometric Study
Bibliographic record
Abstract
BACKGROUND: Achieving symmetry in secondary cleft rhinoplasty is challenging even for the most skilled surgeon. The authors present their technique and outcomes of using a circumferential alar rim graft to relocate the cleft-sided anterior ala superiorly and anteriorly. The graft, under tension, is anchored to the septum at one end and the intermediate crus at the other, exerting an upward and anteriorly projected force. METHODS: Adult patients with unilateral cleft lip and palate who underwent secondary cleft rhinoplasty between 2013 and 2022, using the circumferential alar rim graft (CARG) technique, were included. Standardized photographs were used to obtain morphometric measurements. Ratios of cleft side to non-cleft side nostril height, width, and area, as well as tip projection, were compared preoperatively and postoperatively. Wilcoxon signed-rank test evaluated statistically significant differences between the measurements. RESULTS: Twenty-seven patients were included, with a mean age of 26.9 years (range 19-42). Most were female (n=18), with left-sided clefts (n=20). Nostril height symmetry, expressed as a ratio of cleft side over the non-cleft side (with 1 being perfect symmetry), improved significantly by 10% ( P <0.001). Nostril width symmetry improved significantly by 22% ( P =0.012), and nostril area symmetry improved significantly by 21% ( P =0.008). When preoperative and postoperative subnasale-pronasale over inter-alar width ratios were compared, tip projection was found to increase significantly by 10% ( P =0.029). Five CARG-related complications occurred, including 2 graft detachments and 1 graft fracture. CONCLUSIONS: Using a circumferential alar rim graft in secondary cleft rhinoplasty significantly improved nasal symmetry in terms of nostril height and area.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".