A novel approach to earlobe reconstruction using the V to Y advancement flap
Bibliographic record
Abstract
BACKGROUND: The V to Y advancement flap offers an excellent option for reconstructing defects of the lobule and adjacent structures of the external ear. We demonstrate its utility for small defects of the earlobe including those extending to the antitragal and conchal bowl regions. To our knowledge use of this technique for earlobe reconstruction has not been reported. METHODS: A review of the literature was performed on the use of the V to Y flap for earlobe reconstruction. We then described its use in reconstructing lobular defects in 6 patients. All patients had a non-melanoma skin cancer involving the earlobe. All surgeries were performed under local anesthetic at a tertiary care centre in Halifax, Canada. Defects ranged in size from 1.0 to 1.4 cm. All defects were reconstructed with only a V to Y advancement flap. Patient photographs were taken intra-operatively and post-operatively. For all patients, satisfaction of the final aesthetic result was assessed on a 10 point scale in follow-up at 6 months. RESULTS: A review of the literature did not reveal any reports of the V to Y flap used in isolation for lobular reconstruction. At our centre from 2018 to 2020, this method was well tolerated under local anesthetic in 6 patients with non-melanoma skin cancers of the earlobe. All patients reported an aesthetically satisfying result at 6 months with scores ranging between 8 and 10. Scarring in all cases was minimal. CONCLUSION: The V to Y advancement flap is a simple technique for reconstructing small defects of the lobule. This method is technically straight-forward, poses minimal risk to the patient, and in our experience, yields a favourable cosmetic outcome.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".