Bibliometric Analysis of Platelet-Rich Plasma Treatment for Hair Restoration, Facial Rejuvenation, Dental Procedures, and Gynecological Rejuvenation
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
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.
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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 | 0.015 | 0.023 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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