Reconstructive Techniques in Pediatric Congenital Microtia: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Autografts and allografts are commonly used in microtia reconstruction. We aimed to systematically review and compare these reconstructive materials in pediatric congenital microtia reconstruction. A systematic review of the literature was performed. MEDLINE, Embase, PubMed, Web of Science, and CINAHL databases were searched for original studies on congenital microtia reconstruction in pediatric patients since database inception to 2021. Microtia grade was stratified as high or low. Meta-analysis of pooled proportions and continuous variables was performed using inverse variance weighting with a random effects model to compare between the autograft and allograft groups. Sixty-eight studies with a total of 5,546 patients used autografts (n = 5,382) or alloplastic implants (n = 164). Four other studies used prosthesis, cadaveric homografts, or tissue engineering. The allograft group was on average younger than the autograft group (8.4 vs. 11.1 years). There were no syndromic patients in the allograft group, compared to 43% in the autograft group. Patients treated with allografts had higher microtia grade than those treated with autograft (98 vs. 72%). Autografts were more commonly utilized by plastic surgeons and allografts by otolaryngologists (95 vs. 38%). No autografts and 41% of allografts were done concurrently with atresiaplasty or bone conduction implant. Satisfaction rates were similarly high (>90%) with similar complication rates (<10%). Microtia reconstruction using autografts and allografts had similar satisfaction and complication rates. Allografts were preferred for younger patients and concurrent hearing restoration. Further large-scale studies are required to evaluate the long-term efficacy of these reconstructive techniques.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".