Platelet‐rich fibrin: A review of its role as a new treatment in androgenetic alopecia
Bibliographic record
Abstract
BACKGROUND: Androgenetic alopecia (AGA) is a major cause of hair loss resulting from a complex interplay between various genes and hormones with the result being follicular miniaturization and altered hair cycle dynamics. Platelet-rich plasma (PRP) has a well-established role as adjunctive therapy in AGA but there are many limitations of it. In an attempt to overcome the shortcomings of PRP, liquid platelet-rich fibrin (PRF) was developed. AIM: This article critically reviews the protocol for the preparation and clinical outcomes of PRF. PATIENTS/METHODS: The articles published so far in the English language regarding the preparation and clinical outcomes of PRF were reviewed. RESULTS: Among five studies analyzing various centrifugation speeds and centrifugation times, three of the studies favored low-speed centrifugation, whereas two studies did not support this methodology. A horizontal centrifuge may be preferred over a fixed-angle centrifuge for PRF. Five clinical studies on the use of PRF showed a significant effect on AGA. CONCLUSION: At present, there is no consensus regarding the preparation of PRF. Most studies used fixed-angle centrifugation favored low centrifuge speed and less centrifugation time. Larger studies are needed to determine the optimal centrifugation force and time. A horizontal centrifuge may be preferred over a fixed-angle centrifuge due to the higher yield of platelets, and lesser shear trauma to the cells. In addition, larger, well-designed studies are needed to confirm the benefits of PRF over PRP.
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How this classification was reachedexpand
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) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".