681 A Systematic Review of Fat Grafting in the Management of Burn Scars
Bibliographic record
Abstract
Abstract Introduction Secondary management of thermal injuries remains a challenging and relevant topic for plastic surgeons. Burn scars are associated with both functional and aesthetic issues for patients. While various treatments exist, there is no gold standard of treatment. Fat grafting has been used in multiplicities in reconstruction, and studies have demonstrated improvement in skin texture and contour following infiltration. This systematic review aims to examine all available evidence on outcomes following fat grafting for management of burn scars. Methods A search of Medline, EMBASE, and Cochrane library database was conducted from their inception until June of 2023. Published articles examining outcomes of fat grafting for thermal injury scars were identified, screened, and extracted as per PRISMA guidelines. Results A total of 10457 articles were screened, yielding 14 eligible studies for data extraction accounting for 885 patients. All studies found improvement post-operatively. 9 out of 14 studies used a subjective clinical assessment, one study did not report pre-treatment measurements, the other 8 all found improved outcome based on clinician assessment. One study reported VSS and another reported modified VSS. 3 studies utilized POSAS, the mean difference was an improvement of 7.28 (MCID < 1). Conclusions Further studies, particularly prospective in nature, with standardized outcome measurements are needed to substantiate clinical improvement observed. The authors recommend utilizing either the POSAS or VSS for future studies investigating burn scar treatments. Applicability of Research to Practice This study investigates all concurrent and relevant studies looking at utilizing fat grafting in the management of burn scars. Evidence present is overall supportive of its efficacy. However, meaningful analysis was precluded due to inconsistent outcome reporting. We recommend all studies going forward on this topic to use established tools or PROMs such as the POSAS or VSS. Funding for the Study N/A
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".