Autologous Craniofacial Fat Grafting in the Irradiated Field
Bibliographic record
Abstract
ABSTRACT: Autologous fat grafting has been used as a reconstructive modality following the treatment of head and neck malignancy. However, it has been criticized for poor graft retention and unpredictable results, which may be further compromised by prior radiation therapy. This systematic review will consolidate the literature on autologous fat grafting in the previously irradiated craniofacial region and report its effects on aesthetic and functional outcomes, volume resorption, and postoperative complications. A computerized search of Medline, Embase, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, and Web of Science was performed. Screening and data extraction were performed in duplicate. Data were extracted from the included articles, and outcomes were analyzed categorically. Sixty patients from six studies were included. Mean age was 46.06 years (range 13-73) and 37.5% were female. All studies used the Coleman technique fat grafting or a modified version. A total of 94.9% of patients had significant improvement in aesthetic outcomes and 86.1% in the study specific functional outcomes. Mean graft volume resorption was 41% (range 20%-62%) and there were three (5%) postoperative complications. Autologous fat grafting is increasingly being used to optimize aesthetic outcome following head and neck reconstruction, even in the presence of prior radiation treatment. Although the literature to date is encouraging, the heterogeneity in patient population, intervention, outcome measures, and time horizon limit our ability to draw conclusions about the success of craniofacial fat grafting in the irradiated field. Future research should include a large comparative study as well as a protocol for standardizing outcome measures in this 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".