Radiofrequency Microneedling: A Comprehensive and Critical Review
Bibliographic record
Abstract
BACKGROUND: Many studies have evaluated radiofrequency microneedling (RFMN) in various dermatologic conditions. However, the efficacy and safety of RFMN, and how it compares with other energy-based devices in a clinician's armamentarium, remains unclear. OBJECTIVE: To review higher-quality evidence supporting RFMN and the dermatologic conditions which it can be used in. MATERIALS AND METHODS: A search was conducted in MEDLINE and EMBASE from inception to May 13, 2020, using the terms: "radiofrequency microneedling" OR "fractional radiofrequency" OR "radiofrequency needling" OR "radiofrequency percutaneous collagen induction." Only randomized, split body or blinded studies with original data on humans were included. Non-English or non-dermatology-related studies were excluded. RESULTS: Forty-two higher-quality studies were included after applying the inclusion and exclusion criteria. There were 14 studies for skin rejuvenation, 7 for acne scars, 6 for acne vulgaris, 5 each for striae and axillary hyperhidrosis, 2 for melasma, and 1 each for rosacea, cellulite, and androgenetic alopecia. CONCLUSION: Radiofrequency microneedling is an effective intervention that can be used repeatedly and safely in combination with other treatment modalities and in individuals with darker skin phototypes. Radiofrequency microneedling-induced dermal remodeling and neocollagenesis are slow and progressive but continue to improve even 6 months after treatment.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".