Evaluation of the Efficacy and Safety of Autologous Adipose Tissue-Derived Stem Cells in Treatment of Keloids
Bibliographic record
Abstract
Background: The formation of keloids is accompanied by undesirable aesthetic and psychological impacts. Different therapeutic techniques, including local injection, occlusive dressings, surgical excision, and lasers have been examined for keloids. This work objects to evaluate the efficacy and the safety of autologous adipose tissue-derived stem cells (ADSCs) in keloids treatment. Methods: This prospective clinical research involved 15 subjects with keloids who were injected with autologous ADSCs three sessions at monthly intervals. Follow up was done for 3 months after treatment and evaluation was done for improvement in Vancouver scar score, patient's opinion and physician's opinion. Results: In the studied patients, 8 patients (53.3%) showed good improvement (25 – 49%), 7 patients (46.7%) showed very good improvement (50 – 74%) and none of the patients (0%) showed excellent improvement. Side effects were mild and tolerable and included pain during injection and abdominal discomfort for few days after lipoaspiration. Conclusions: Adipose-derived stem cells are effective, safe and are of more value in improving consistency and vascularity of keloids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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 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".