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Record W4389977954 · doi:10.1097/dss.0000000000004034

Same But Different: An 18-Month Cross-Sectional Study of Cosmetic Procedures in Patients With Skin Phototype I–III Versus IV–VI in Toronto, Canada

2023· article· en· W4389977954 on OpenAlexaffabout
Katherine McDonald, Renée A. Beach

Bibliographic record

VenueDermatologic Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsPhototypeMedicineCohortContext (archaeology)Skin typeCross-sectional studyHyperpigmentationSurgeryDermatologyCohort studyInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Patients with darker skin phototypes self-report less facial aging than their lighter-skinned counterparts. However, the association of skin phototype with the type of cosmetic procedures received, is yet to be established in a Canadian context. OBJECTIVE: To compare the pattern of nonsurgical cosmetic procedures performed on people with different Fitzpatrick SPTs. MATERIALS AND METHODS: Cross-sectional study of patient encounters from October 2020-April 2022. Charts and photographs were reviewed and analyzed for age, sex, SPT, and procedure type. Participants were stratified by SPT into 2 cohorts: SPT I-III and SPT IV-VI. SPTs were collapsed into groups based on definitions of "skin of color" (SPT IV-VI) in previous literature. RESULTS: We analyzed 350 patients with mean age 43.4, of whom 320 (91%) were female and 30 (9%) were male. The SPT I-III cohort was older (mean age 45 vs 38.5 years, p < .0001) and more frequently underwent neuromodulator injection. The SPT IV-VI cohort more frequently underwent microneedling, platelet-rich plasma, or electrodessication. CONCLUSION: There are distinct patterns of cosmetic procedures performed. The SPT I-III cohort more commonly received procedures to manage facial aging. The SPT IV-VI cohort was younger and more commonly underwent procedures to manage hyperpigmentation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.321
Teacher spread0.281 · 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 designObservational
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

Citations0
Published2023
Admission routes2
Has abstractyes

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