MICRONEEDLING - A FORM OF COLLAGEN INDUCTION THERAPY - OUR FIRST EXPERIENCES.
Bibliographic record
Abstract
INTRODUCTION: Microneedling (percutaneous collagen induction therapy) is a new promising miniinvasive therapeutic method for the treatment of skin alterations of different aetiology, including burn scars. Since 2016, it is also available at our department. The microtraumatization of scars with the Dermaroller® leads to an activation of the healing cascade, activation of growth factors, which activate cell proliferation in the wound, increased synthesis and deposit of collagen - elastin complex with successive transformation of collagen III to collagen I, to neoangiogenesis and thus to accelerated scar remodelling. MATERIAL AND METHODS: In the pilot study conducted in 2016, the microneedling method with Dermaroller® with 2.5 mm needles was used in six patients (two males, four females; age 25-73 years) with stabilized scars after previous application of split thickness skin graft due to thermal injury. We repeated the microneedling procedure in three intervals approximately 6 to 8 weeks apart, with the use of topical anaesthesia. RESULTS: Preliminary results showed a subjective improvement of the scars. Objective evaluation with the Vancouver Scar Score showed an improvement of an average of two points before and after treatment. CONCLUSION: Our first clinical experience show that microneedling appears to be a suitable microinvasive method for the improvement of scar quality after burn trauma.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".