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Record W2750701682 · doi:10.1097/dss.0000000000001315

Facial Soft Tissue Augmentation in Males: An Anatomical and Practical Approach

2017· review· en· W2750701682 on OpenAlexaff
Anthony Rossi, Rebecca C. Fitzgerald, Shannon Humphrey

Bibliographic record

VenueDermatologic Surgery · 2017
Typereview
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSoft tissueFace (sociological concept)Cosmetic TechniquesSkin AgingNatural (archaeology)MedicineComputer scienceSurgeryDermatologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Soft tissue augmentation is increasingly being performed to treat male facial aging. OBJECTIVE: To review anatomical consideration specific for the male face and define an approach to male soft tissue augmentation that creates harmonious and natural results. CONCLUSION: Addressing aesthetic concerns in the upper face, midface, and lower face should be performed in concert with a combinatorial and pan facial approach. Correct filler placement at different areas of concern is important to achieve desired and natural results. Dermal and subcutaneous fillers can be used successfully in the male patient to enhance facial features, correct volume loss, and improve features of aging.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.253
GPT teacher head0.465
Teacher spread0.212 · 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.

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

Citations16
Published2017
Admission routes1
Has abstractyes

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