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 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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".