Promises and Pitfalls of Regenerative Therapies for Androgenetic Alopecia: Platelet-Rich Plasma, Photobiomodulation, Stem Cells, and Exosomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Regenerative therapies have emerged in recent years. In particular, their utility in managing androgenetic alopecia-the most prevalent hair loss condition worldwide, affecting up to half of adults-is an active area of research. Navigating this space can be challenging for physicians due to widespread commercialization, lack of high-quality evidence, and an evolving regulatory landscape. OBJECTIVE: To critically review recently published evidence (2020-2025) on platelet-rich plasma, photobiomodulation, stem cells, and exosomes for the treatment of androgenetic alopecia. METHODS: A scoping review was conducted using PubMed, Embase (Ovid) and the Cochrane Controlled Register of Trials in February and November of 2025. Combination therapies were excluded. RESULTS AND CONCLUSIONS: Platelet-rich plasma is the most studied modality, with emerging investigations into newer formulations such as leukocyte-rich and pure platelet-rich plasma. However, recent studies are limited by inconsistent reporting of cellular composition, short follow-up durations, and a lack of comparative data on treatment protocols. The efficacy of photobiomodulation as a monotherapy remains debated, with inconsistent reporting of device parameters. Stem cells and exosomes show promising, though still limited, clinical evidence in inducing hair regrowth. Standardization of these therapies is crucial, with emphasis on transparency, reproducibility, and patient safety.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it