Use of growth factor concentrate using derma roller in treatment of androgenetic alopecia: A literature review
Bibliographic record
Abstract
Androgenetic alopecia (AGA) is a common cause of hair loss in both men and women. Recent advances in regenerative medicine have led to the use of platelet-rich plasma (PRP) combined with micro-needling as a potential treatment for AGA. However, the clinical effectiveness of this combination therapy remains unclear. To evaluate the efficacy and safety of platelet-rich plasma (PRP) combined with micro-needling for the treatment of androgenetic alopecia through a systematic review and meta-analysis of available clinical studies. A systematic search was conducted in PubMed, Embase, and the Cochrane Library for studies published up to January 2024. Inclusion criteria were randomised controlled trials (RCTs) and cohort studies that assessed hair density, hair thickness, or patient satisfaction after treatment with PRP and micro-needling. Data were extracted and pooled using a random-effects model, and heterogeneity was assessed with the I² statistic. The quality of the studies was evaluated using the Cochrane Risk of Bias tool for RCTs and the Newcastle-Ottawa Scale for cohort studies. A total of 12 studies involving 546 participants were included. Meta-analysis showed that the combination of PRP and micro-needling significantly increased hair density (mean difference: 18.9 hairs/cm², 95% CI: 15.3-22.5, p < 0.001) and hair thickness (mean difference: 0.4 mm, 95% CI: 0.3-0.5, p < 0.01) compared to controls. Patient satisfaction was also higher in the treatment group. The most commonly reported adverse effects were mild scalp irritation and transient erythema, with no serious adverse events. Moderate heterogeneity was observed among the studies (I² = 42%). The combination of PRP and micro-needling is an effective and well-tolerated treatment for androgenetic alopecia, resulting in significant improvements in hair density and thickness. Further large-scale, high-quality trials are needed to confirm these findings and standardize treatment protocols.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".