Evaluation of Clinical and Ultrasound Changes With the Use of Microneedling Versus Fractional CO2 Laser in Atrophic Acne Scars
Bibliographic record
Abstract
INTRODUCTION: Atrophic acne scarring, a common sequela of acne, can be treated by different interventions, including microneedling and laser resurfacing. OBJECTIVES: We sought to evaluate the comparative efficacy of microneedling versus fractional CO2 laser in treating atrophic acne facial scars using imaging with high and ultra-high frequency ultrasound. METHODS: Participants received 2 sessions, separated by 1 month, of microneedling on the left side of the face and fractional CO2 laser on the right. Color Doppler ultrasound evaluations (24 and 70 MHz) were conducted at baseline and 3 months after treatment. Each patient completed questionnaires on satisfaction, pain, and adverse effects. RESULTS: Nine subjects were enrolled. The frequency order of scar types was boxcar, ice-pick, and rolling. At 3 months, using the acne scar clinical evaluation scale, a decrease in scar scores of both methods was observed for total scars (P = 0.0005), ice-pick scars (P = 0.0128), and rolling scars (P = 0.0007). Twenty-two scars analyzed by ultrasound demonstrated a trend to decrease in size; however, no significant changes were observed for either microneedling or CO2 laser treatments. Moreover, there were no significant differences between these methods. Both treatments were rated as good or very good by patient assessments. There was a low frequency of pain and hyperpigmentation reported with both modalities, albeit somewhat higher with microneedling. CONCLUSIONS: Both microneedling and CO2 laser improved atrophic acne scars. Ultrasound did not show significant differences between these modalities.
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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 | 0.002 |
| 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".