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Record W3203396203 · doi:10.1097/dss.0000000000003239

The Emerging Field of Regenerative Aesthetics—Where We Are Now

2021· article· en· W3203396203 on OpenAlexaff

Bibliographic record

VenueDermatologic Surgery · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsField (mathematics)Regenerative medicineEmerging technologiesMEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND: Regenerative aesthetics is an emerging branch of regenerative medicine with therapies aimed at recapturing youthful structure and function using the body's own systems. OBJECTIVE: To introduce the field of regenerative aesthetics, and to explore themes and evidence surrounding current and emerging therapies in the field. MATERIALS AND METHODS: A review of the literature was performed for each of the 3 pillars of regeneration; namely, stem cells, biochemical cues, and scaffolds. RESULTS: Herein, we provide an overview of the field of regenerative aesthetics, a discussion surrounding the 3 pillars of regeneration, and an overview of the evidence supporting current and emerging therapeutic modalities that could play a pivotal role in the future of aesthetic treatments. CONCLUSION: An enhanced understanding of this field can serve to further enhance our awareness about the regenerative effects of therapies we already offer, in addition to providing inspiration for future innovation.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.007
Scholarly communication0.0060.010
Open science0.0010.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0100.002

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 head0.020
GPT teacher head0.277
Teacher spread0.257 · 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

Citations27
Published2021
Admission routes1
Has abstractyes

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