Clinical and histochemical response to automated microneedling therapy in treatment of traumatic scars
Bibliographic record
Abstract
Background: Post traumatic skin injuries are challenging to manage. Patients may have erythematous, hypertrophic, or atrophic scars. Microneedling therapy is minimally invasive non-surgical and non-ablative procedure used for skin rejuvenation that relies on the principle of neocollagenesis. Aim: We aimed to assess the clinical and histochemical response to automated microneedling therapy in treatment of traumatic scars. Methods: This prospective study included twenty patients with traumatic scars. All patients received 4 monthly sessions of automated microneedling therapy. Outcome assessment included modified Vancouver Scar Scale, digital photographic documentation and patient's satisfaction. Histochemical evaluation by quantitative morphometric assessment for collagen and elastic fibers using image analyzer performed before and 3 months after treatment for Masson’s trichrome and Orcein stained sections respectively. Results: There was statistically significant improvement in scar vascularity (p= 0.018), scar pigmentation (p= 0.008), and scar pliability (p= 0.002) and sum of mVSS (P=0.000002). Histochemically, there was significant increase in collagen content, (p= 0.023), and elastin content (p= 0.003) as quantified by image analyzer. There was no significant correlations (r: 0.158 and -0.259; p-values: 0.55 and 0.34) between micro-needling therapy and scar type (atrophic versus hypertrophic).
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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 | 0.001 |
| 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".