Management of Congenital Auricular Anomalies
Bibliographic record
Abstract
LEARNING OBJECTIVES: After studying this article, the participant should be able to: 1. Describe normal ear anatomy and development, and evaluate the patient's ears for differences in shape, size, prominence, and symmetry. 2. Identify common congenital ear deformities, including prominent ear, macrotia, Stahl ear, cryptotia, constricted ear, and lobule anomalies. 3. Describe both early nonoperative management and operative techniques for correction of these ear deformities. 4. Be aware of advantages and disadvantages of common and emerging techniques for correction of pediatric ear deformities. SUMMARY: Whereas severe ear malformations such as microtia/anotia are rare, other ear deformities, such as prominent ear, Stahl ear, and cryptotia, are common. Although these ear deformities result in minimal physiologic morbidity, their psychological and cosmetic impact can be significant. Identifying these common deformities and understanding how they differ from normal ear anatomy is critical to their management. In cases where a deformity is identified in neonatal life, ear molding may obviate the need for surgery. Although various surgical techniques have been described for correction of common ear deformities, the surgeon should follow a careful stepwise approach to address the auricular deformity or deformities present. By using such an approach, complications may be minimized and predictable aesthetic outcomes achieved.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".