Surgical Microtia Reconstruction in Hemifacial Microsomia Patients: Current State and Future Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it