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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".