Bibliographic record
Abstract
Introduction Post-burn hypertrophic scars are an important cause of physical discomfort, limitation of movements, psychological disorders, low self-esteem and reduced quality of life. Treatment for this condition is complex and involves several options. Microneedling, also known as minimally invasive percutaneous collagen induction, is an affordable minimally invasive option that can be combined with other treatments, including ablative ones.Objective The goal of this study was to present our microneedling approach for the treatment of hypertrophic scars after burns.Method A prospective study of 15 patients with post-burn hypertrophic scars was conducted between October 2016 and June 2022. All patients were treated with microneedling and drug delivery of triamcinolone. Scars were evaluated using Vancouver Scar Scale (VSS), Burn Scar Assessment Scale (BSAS) and angle measurement for amplitude of movement evaluation of joints.Results A significant improvement in the VSS score was obtained after microneedling (8.8 +/- 0.44 to 4.1 +/- 0.98; p = 0.012), especially in the acute group (less than 1 year after burns): 9.3 +/- 0.49 to 3.5 +/- 1.36; p = 0.041. There was a significant and progressive improvement of the scars throughout the treatment sessions in all criteria evaluated and in the ranges of joint movement (p = 0.012).Conclusion Our microneedling protocol promoted a significant improvement of post-burn scars, especially in acute hypertrophic scars, and in the amplitude of joint motion. Sequential treatments provided progressive improvement.Level of Evidence IVThis 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 | 0.000 | 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".