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Record W2520975065 · doi:10.1097/dss.0000000000000901

A Mechanistic Model of Platelet-Rich Plasma Treatment for Androgenetic Alopecia

2016· review· en· W2520975065 on OpenAlexaff
Aditya Gupta, Jessie Carviel

Bibliographic record

VenueDermatologic Surgery · 2016
Typereview
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsMediprobe Research (Canada)University of Toronto
Fundersnot available
KeywordsPlatelet-rich plasmaWnt signaling pathwayMAPK/ERK pathwayHair follicleAngiogenesisMedicineCancer researchSignal transductionProtein kinase BGrowth factorKinaseProtein kinase ACell biologyPharmacologyInternal medicineBioinformaticsEndocrinologyReceptorBiologyPlatelet

Abstract

fetched live from OpenAlex

BACKGROUND: Platelet-rich plasma (PRP) therapy is a novel procedure used to treat androgenetic alopecia (AGA). OBJECTIVE: Propose a mechanism of action of PRP therapy for AGA. METHODS AND MATERIALS: A thorough literature search including PRP research for AGA therapy as well as PRP research in other areas of medicine was conducted. RESULTS: A mechanistic model for the action of PRP on the hair follicle was created. CONCLUSION: Platelet-rich plasma therapy stimulates hair growth through the promotion of vascularization and angiogenesis, as well as encourages hair follicles to enter and extend the duration of the anagen phase of the growth cycle. The process is accomplished through growth factor-mediated increased activation of wingless (Wnt)/β-catenin, extracellular signaling regulated kinase (ERK), and protein kinase B (Akt) signaling pathways, which leads to the necessary cellular proliferation and differentiation.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.131
GPT teacher head0.338
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations98
Published2016
Admission routes1
Has abstractyes

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