Maintenance of the anatomic contours in auricular reconstruction: The button technique
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Reconstructing the contours of the auricle is a unique challenge. Various bolster techniques have been tried to help prevent complications such as hematoma, seroma, and morbidity. Here, we describe a simple technique using a button to maintain the natural ear contour when it is at risk of a poor aesthetic outcome. MATERIALS AND METHODS: A 77-year-old man underwent resection of a squamous cell carcinoma of the postauricular skin on the right ear, which involved the helical margin. A skin graft was chosen to close the defect. However, on initial inspection of the repair, buckling of the scaphoid fossa, collapse of the antihelical fold, and notching of the helix were observed. When these buckling changes persisted even after the anesthesia-related swelling resolved the following day, a button bolster was placed for 2.5 weeks to provide support for the cartilage. RESULTS: Standardized digital imaging revealed maintenance of the original contours and sulci of the ear with an excellent cosmetic result. CONCLUSION: Recreation of the auricular contours is critical for an excellent cosmetic outcome. Using a button bolster is worth considering as it is of low cost, can easily fit into the natural ear contours, and can provide a rigid structure to ensure maintenance of the ear shape.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".