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Record W2342656642 · doi:10.1097/dss.0000000000000747

Combination Therapy in Midfacial Rejuvenation

2016· review· en· W2342656642 on OpenAlexaff
Shannon Humphrey, Katie Beleznay, Rebecca C. Fitzgerald

Bibliographic record

VenueDermatologic Surgery · 2016
Typereview
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRejuvenationMedicineFacial rejuvenationPatient satisfactionAttractivenessFacial attractivenessCosmetic TechniquesOrthodonticsSurgeryPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Facial aging in the midface reflects cumulative results of multiple intrinsic and extrinsic factors over time. Midfacial rejuvenation procedures can make a positive impact on facial attractiveness and patient satisfaction. OBJECTIVE: To review evidence and clinical experience using combination treatments for midfacial rejuvenation to achieve optimal outcomes. MATERIALS AND METHODS: This article provides a review of published scientific evidence supporting the use of combination therapy in midfacial rejuvenation. In addition, the authors share their cumulative clinical experience and best practices for combination treatments in the midface. RESULTS: Clinical experience and evidence shows that combining multiple aesthetic therapies targeting multiple aspects of the aging process provides optimal results, with greater overall efficacy and a higher level of patient satisfaction.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.116
GPT teacher head0.376
Teacher spread0.260 · 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
Published2016
Admission routes1
Has abstractyes

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