A Study on the Nonsurgical Correction Treatment Age Window and Long-Term Follow-Up of Infants With Congenital Ear Anomalies in China
Bibliographic record
Abstract
OBJECTIVES: To evaluate the efficacy of ear molding across various initial ages and analyze challenges encountered by infants beyond the optimal treatment age window. METHODS: A retrospective review of 331 infants (527 ears) treated with EarWell was conducted over 5 years from January 2017 to March 2022 at a single center. The treatment duration of the ear molding, success rate, recurrence rate, and complication rate were analyzed among the 3 age groups. Concentrate on evaluating treatment outcomes for infants with an initial age exceeding 42 days. RESULTS: The mean age at initial treatment was 25±28 days. In addition, it includes a child with cryptotia who is 3.5 years old (1278 d). The mean duration of treatment was 7±5 weeks. In the long-term follow-up, the overall treatment success rate was 92%, with 467 ears (88.6%) showing improvement without recurrence, 30 ears (5.7%) experiencing varying degrees of recurrence, and 30 ears (5.7%) showing no improvement or complete recurrence. A total of 20 infants (3%) developed mild skin complications during treatment. CONCLUSIONS: Ear molding is a safe and effective option for the treatment of congenital ear anomalies, with a low recurrence rate during long-term follow-up. For infants with congenital auricular anomalies aged over 42 days, ear molding remains a viable option. Treatment success may be influenced by the age at treatment, the subtype of anomalies, and relies on the assessment of a specialized otologist, expert procedural techniques, as well as thorough understanding and cooperation from parents.
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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 | 0.001 | 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) | 0.001 | 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".