Soft-Tissue Reconstruction in Progressive Hemifacial Atrophy: Current Evidence and Future Directions
Bibliographic record
Abstract
BACKGROUND: Progressive hemifacial atrophy is a rare disorder characterized by gradual unilateral soft-tissue atrophy in the face, which may also include clinically significant degeneration of underlying muscle and bone. In recent years, there has been a growing body of evidence regarding different soft-tissue reconstructive strategies in progressive hemifacial atrophy and the impact of intervention timing on disease progression. This article provides a comprehensive synthesis of the latest evidence to guide optimal management. METHODS: A comprehensive multidatabase search was performed through April of 2020 using relevant search terms to identify clinical studies. Outcomes, complications, and disease- and patient-related indications pertaining to different soft-tissue reconstructive strategies in progressive hemifacial atrophy were collected and critically appraised. RESULTS: Thirty-five articles reporting on a total of 824 progressive hemifacial atrophy patients were evaluated; 503 patients (61 percent) were managed by microvascular free flaps, 302 patients (37 percent) were managed by autologous fat grafts, and 19 patients (2 percent) were managed by pedicled flaps. A detailed synthesis of outcomes is presented in this article, as is a comparative evaluation of different microvascular free flap options. CONCLUSIONS: Soft-tissue reconstruction in progressive hemifacial atrophy remains an evolving field. Operative decision-making is often multifaceted, and guided by specific volumetric, aesthetic, and functional deficiencies. Serial fat grafting is the primary modality used for patients with mild soft-tissue atrophy, whereas microvascular free flaps widely remain the treatment of choice for reconstruction of large-volume defects. There exists a growing role of graft supplementation to improve fat graft survival, whereas recent evidence demonstrates that early intervention may help curb disease progression.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".