Early Intervention with Highly Condensed Adipose-Derived Stem Cells for Complicated Wounds Following Filler Injections
Bibliographic record
Abstract
BACKGROUND: A rise in cosmetic procedures has seen the use of fillers become more prevalent. Complications resulting from use of fillers have prompted introduction of various medical and surgical interventions. Recently, stem cell therapies have become more widely used as a new treatment option for tissue repair and regeneration. METHODS: We utilized adipose-derived stem cells (ASCs) for tissue regeneration in patients with filler-related complications such as necrosis. All 12 patients were treated with ASCs and some patients had additional treatment. After relief of symptoms, wound surface area was compared in terms of pixel numbers and scar condition was evaluated using the Vancouver Scar Scale (VSS). RESULTS: In general, we achieved satisfactory resolution of filler-related complications in a short period of time without serious side effects. The average number of days from stem cell treatment to symptom relief was 7.3 days. The proportion of wound surface area from photographic record was 4.39 % before treatment, decreasing considerably to 1.01 % following treatment. Last, the VSS showed almost all patients scored below 3, with two patients receiving scores of 7 and 8; the average score was 2.78 (range from 0 to 8). CONCLUSIONS: ASCs are a new treatment option for post-filler injection wounds such as necrosis. Using stem cells, we were able to obtain satisfactory results in a short period of time without complications requiring surgical procedures. We suggest stem cell injections could be used as the first option for treatment of complications from filler injections. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".