Delayed Reconstruction of Ear Lobule With Conchal Cartilage Rotational Flap
Bibliographic record
Abstract
BACKGROUND: The earlobe plays a critical role in facial aesthetics and balance. Large, full-thickness defects of the earlobe present a unique reconstructive challenge due to the absence of cartilage and the need for long-term volume and contour maintenance. OBJECTIVE: To describe a novel 2-phase surgical technique for earlobe reconstruction utilizing a cartilage graft. METHODS: Between 2010 and 2019, 12 patients underwent earlobe reconstruction using this technique. In the first phase, a cheek flap was created, and a cartilage graft was harvested and implanted into the lobular defect. Six weeks later, the second phase involved refining the lobule with a V-Y advancement flap and grafting the posterior aspect with a full-thickness skin graft. Patient outcomes were evaluated based on patient satisfaction and complication rates. RESULTS: The cohort included 12 patients aged 29 to 82 years, comprising congenital deformities (n=3), traumatic arteriovenous malformations (n=2), and post-resection defects from skin malignancies (n=7). The technique was successfully applied across all cases, achieving aesthetically pleasing results with no recorded complications. CONCLUSION: This 2-phase reconstruction technique offers a reliable and effective solution for large earlobe defects. Further studies with larger cohorts and extended follow-up periods are needed to validate these findings.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".