Surgical Microtia Reconstruction in Hemifacial Microsomia Patients: Current State and Future Directions
Bibliographic record
Abstract
Background: Hemifacial microsomia (HFM) is one of the most common congenital craniofacial disorders. Among many other features, microtia is present in the large majority of these patients. However, mainly due to the unilateral hypoplastic anatomy, microtia reconstruction among this patient population remains a reconstructive challenge for plastic surgeons. Given that no clear standards exist, an evidence-based synthesis of the literature was devised. Methods: A systematic search of Pubmed, Medline, and Embase was carried out, in accordance with the PRISMA guidelines. Studies discussing surgical microtia reconstruction for HFM patients were retained. Qualitative data regarding study design, challenges addressed, specific recommendations, and their respective strengths/limitations were extracted from each. Retrieved recommendations were consolidated and assigned a level of evidence grade. Results: Although only 11 studies were included in this review, these provided 22 main recommendations regarding the eight HFM-specific challenges identified, which were of either grade C (n = 5) or D (n = 17). Included studies addressed construct location (n = 7), the low hairline (n = 6), soft tissue construct coverage (n = 6), earlobe reconstruction (n = 6), construct projection (n = 5), anomalies of the relevant neurovascular systems (n = 2), retroauricular construct coverage (n = 2), and sizing of the construct (n = 2). Conclusions: Given the many persisting reconstructive challenges regarding surgical microtia reconstruction for HFM patients, the authors present a comprehensive and evidence-based consolidation of recommendations specific to these challenges. The authors hope this systematic review can appropriately guide plastic surgeons and will ultimately improve care for this patient population.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".