Bibliometric Analysis of Platelet-Rich Plasma Treatment for Hair Restoration, Facial Rejuvenation, Dental Procedures, and Gynecological Rejuvenation
Bibliographic record
Abstract
Introduction: Autologous platelet-rich plasma (PRP) technologies offer an attractive treatment option for various medical fields. Owing to its high concentration of growth factors, PRP has been posited to induce proliferation, differentiation, and angiogenesis at the cellular level, as well as wound-healing and remodeling at the tissue level. The goal of the present bibliometric analysis was to characterize the growing body of literature concerning PRP use in various medical applications. Methods: A comprehensive literature search was performed on June 28, 2024, using Web of Science and SCOPUS databases, covering all available publications in selected categories from 2001 to present. Results: PRP use for hair restoration had both the greatest number of total publications among the investigated applications, whereas PRP use in dental procedures had the longest-standing history of publications. PRP use in hair restoration and facial rejuvenation had the greatest number of placebo-controlled and double-blinded randomized controlled trials; however, the impact of results may suffer from a lack of consistency in PRP preparation and outcome measurement between different studies. Conclusion: To effectively validate the utility of PRP across various medical interventions, careful consideration of methodology should be undertaken for future studies to ensure validity of results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.164 | 0.202 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".