Nanofat injection for the treatment of abnormal scar compared with triamcinolone acetonide
Bibliographic record
Abstract
Introduction: Hypertrophic and keloid scars arise from abnormal healing processes in fibrous tissue where tissue repair and regeneration control mechanisms do not work well. The standard treatment is an intralesional steroid injection with triamcinolone acetonide (TA). This method is effective, but there are some side effects include hypopigmentation and tissue atrophy. The nanofat method has been applied to various skin conditions with fewer side effects. Therefore, the use of nanofat with its regenerative properties can be expected to yield better results. This study aims to compare the decrease in modified Vancouver Scar Scale (VSS) score after nanofat administration and TA in abnormal scars. Methods: This is an experimental study with a pre- and posttest control group design. Twenty participants with abnormal scars from the Outpatient clinic of Dr. Soetomo General Academic Hospital, Surabaya, Indonesia, were recruited. Nanofat and TA were applied once. The evaluation of modified VSS was conducted twice (pre- and posttest 8 weeks after therapy). Results: There was a significant decrease in the modified VSS pretest score compared to posttest after nanofat therapy (P = 0.004). There was a significant decrease in the modified VSS pretest score compared to posttest after (P TA therapy = 0.004). There was no significant difference in VSS score between treatment with nanofat compared to TA in patients with abnormal scars. Conclusion: There was a significant decrease in the modified VSS score after nanofat and TA therapy. Nanofat has the same effectiveness as TA for abnormal scar therapy.
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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 | 0.000 | 0.000 |
| 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".