Utilization of Free Soft Tissue Grafts in Otoplasty: A Simple Yet Effective Way to Avoid Suture Extrusion
Bibliographic record
Abstract
BACKGROUND: Prominent ear deformity occurs in 5% of the general population and has been treated by otoplasty for many years to address the psychosocial challenges of having such a deformity. There is extensive literature but no consensus on the best method to address potential surgical complications, including suture extrusion. OBJECTIVES: The aim of this article was to describe a surgical technique designed to reduce suture extrusion following otoplasty surgery by placing free soft tissue grafts between Mustardé sutures and postauricular skin. METHODS: Two hundred and eleven patients who underwent otoplasties with soft tissue grafts between January 2017 and January 2020 were included in this study. All surgeries were performed by 2 facial plastic surgeons with more than 20 years of experience each, practicing in Toronto, Canada. Patients were followed up to assess for suture extrusion between 12 and 36 months (median, 21 months) postoperatively. The rates of suture complications and extrusion were compared with those previously reported in the literature. RESULTS: Only 2 patients out of 211 (0.47%) had unilateral suture extrusion and were treated with suture removal. This is dramatically lower than the upper values reported in the literature, which average 5.55% (range, 0%-22.2%). CONCLUSIONS: A soft tissue graft separating the Mustardé sutures and postauricular skin acts as a barrier, and can be used in conjunction with traditional surgical techniques. By adding this graft in the proposed manner, there is additional tissue reinforcing the suture repair, thereby reducing the rates of suture complications and extrusion without increasing the operative time.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".