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Record W4385603075 · doi:10.3390/dermato3030016

Sex-Dependent Skin Aging and Rejuvenation Strategies

2023· article· en· W4385603075 on OpenAlexaff
Marta Gerasymchuk, Gregory Ian Robinson, Nataliia Vardinska, Samuel Abiola Ayedun, Sandra Chinwe Alozie, J. Wesley Robinson, Olga Kovalchuk, Igor Kovalchuk

Bibliographic record

VenueDermato · 2023
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsWestern UniversityUniversity of Lethbridge
FundersServier
KeywordsRejuvenationSkin AgingMedicineRosaceaDermatologyAcneGerontology

Abstract

fetched live from OpenAlex

The skin, the largest external organ, serves as the primary defensive barrier against various environmental factors such as ultraviolet exposure, pollution, dietary habits, pathogens, and chemical compounds. Consequently, the skin reflects our age through visible signs of aging, such as wrinkles, age spots, dullness, and sagging. This review explores the gender-related aspects of cutaneous aging and the associated dermatological conditions. It highlights the different manifestations of aging in females and males that become evident after the age of 12, emphasizing the susceptibility to conditions such as seborrheic eczema, acne, and rosacea. Treatment strategies often vary between genders due to these disparities. While men tend to experience accelerated skin aging, most anti-aging products and strategies primarily target females. However, there has been a recent shift in men’s priorities, leading to increased interest in maintaining a youthful appearance and seeking cosmetic treatments. The manuscript covers a comprehensive range of modern dermal anti-aging and rejuvenation procedures, including plastic surgery, bio-revitalization methods, lasers, microneedling, and topical treatments. Additionally, it explores promising natural and synthetic therapeutics for combating age-related skin changes. The focus is on understanding the physiological aspects of gender-related cutaneous structure and aging to guide effective and tailored approaches in dermatological practice.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.171

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.292
Teacher spread0.274 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations5
Published2023
Admission routes1
Has abstractyes

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