Single-Stage Autologous Ear Reconstruction for Microtia
Bibliographic record
Abstract
BACKGROUND: The authors have been using the Nagata technique since 2002. In this review of 100 consecutive ear reconstructions, the authors present technique modifications that have evolved over this period that have contributed to improved auricular contour and that now allow for auricular reconstruction in a single stage. METHODS: This study is a retrospective review of a prospectively acquired database. The series is restricted to primary reconstructions performed for congenital microtia. Photographs of 10 consecutive patients are presented to demonstrate the results of the technique. Surgical complication rates are discussed. RESULTS: One hundred ear reconstructions were performed in 96 patients. There were 75 primary cases of congenital microtia. Twenty-four ears underwent a two-stage reconstruction, and 51 ears were reconstructed with a Nagata stage I procedure or a single-stage reconstruction. There was a gradual shift in technique, with a trend to perform fewer Nagata stage II outsetting procedures and more single-stage reconstructions. In patients who underwent an ear reconstruction in two stages, the surgical complication rate was 22 percent. In the last 40 consecutive ear reconstructions since abandoning the two-stage approach, the surgical complication rate is now 15 percent. CONCLUSIONS: A modification of Nagata's technique of autologous ear reconstruction for microtia is described. Modifications of the three-dimensional framework address the contour of the inferior crus and control tragal projection and position. Inclusion of a projection block and recruitment of retroauricular skin allow for symmetric projection of the ear in a single stage. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".