Growth factor concentrations in platelet‐rich plasma for androgenetic alopecia: An intra‐subject, randomized, blinded, placebo‐controlled, pilot study
Bibliographic record
Abstract
BACKGROUND: Platelet-rich plasma (PRP), processed from autologous peripheral blood, is used to treat androgenetic alopecia (AGA). OBJECTIVE: To determine the efficacy of PRP for hair growth promotion in AGA patients in a randomized, blinded, placebo-controlled, pilot clinical trial (NCT02074943). METHODS: The efficacy of an 8 week, five session, PRP treatment course was determined by measuring hair density and hair caliber changes in 10 AGA affected patients. For each PRP sample, the concentrations of selected growth factors were determined using a multiplex assay system. The clinical results were then correlated with the growth factor concentrations in PRP. RESULTS: At 16 weeks, 8 weeks after the last PRP injection, treated areas exhibited increased mean hair density (+12.76%) over baseline compared to placebo (+0.99%). Mean hair caliber decreased in both treated and placebo regions (-16.22% and -19.46%, respectively). Serial analysis of PRP significant variability in concentrations between patients. Overall, there was a positive correlation between GDNF concentration and hair density (P = .004). Trends, though not statistically significant, were also observed for FGF2 and VEGF. LIMITATIONS: Small sample size and lack of comparative cohorts receiving protocol variations limit confidence in the study data. CONCLUSIONS: This small pilot clinical trial suggests PRP treatment may be beneficial for AGA. However, the variable hair growth responses between patients indicate there is a significant opportunity to improve PRP therapy protocols for hair growth promotion. The variability in growth factor concentration in PRP suggests standardization of growth factors postprocessing might improve hair growth responses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".