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Record W4402533120 · doi:10.2147/ccid.s465155

Turn Your AART into a HIT Using a Complete Range of Aesthetic Injectables: Methodology for Combining Products to Maximise Patient Outcomes

2024· article· en· W4402533120 on OpenAlex
Andreas Nikolis, Luiz Eduardo Toledo Avelar, Alessandra Haddad, Stephanie Chuk Kwan Lam, Andrei I. Metelitsa, Heidi Prather, Frank Rosengaus, Kaitlyn M. Enright, Dessislava Lazarova, Inna Prygoya, Fabio Iachetti

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Cosmetic and Investigational Dermatology · 2024
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsUniversity of CalgaryMcGill University
FundersGalderma
KeywordsMedicineRange (aeronautics)Engineering

Abstract

fetched live from OpenAlex

Purpose: Optimizing outcomes of aesthetic treatments with injectable products usually requires a consideration of the entire face to ensure balance, along with combination treatments that align with the patient's goals. To help injectors, a method of assessing the patient and developing an individualized, holistic treatment plan was developed. This methodology is termed Assessment, Anatomy, Range, and Treatment (AART™) and Holistic Individualized Treatments (HITs™). This article aims to describe and evaluate the novel and systematic AART-HIT™ methodology. Methods: The AART-HIT™ methodology, including its associated diagnostic tool the Facial Assessment Scale (FAS™), were developed to aid injectors in completing a patient assessment in which the entire face is evaluated, the relevant anatomy is considered, the science behind the available range of products is understood, and the treatment plan is individualised for the patient. Specifically, the HITs™ are methodologic tools for practitioners to perform a standardized, full facial assessment and to create an individualized treatment approach to holistically address a patient's aesthetic concerns. The use of this methodology in clinical practice was assessed via a survey, deployed to twenty-eight clinicians. Results: Over 85% of participants agreed that the AART-HIT™ methodology was adequate for their needs. Additionally, 100% of participants agreed that the temporal sequencing of HITs™ and the FAS™ diagnostic tool was useful in clinical practice. Furthermore, over 70% of participants agreed that the anatomical locations identified in each HIT™ were sufficient, while over 80% responded that the HITs™ adequately represented the range of products. Finally, over 85% of participants agreed that the HITs™ covered different ethnic skin types and various patient ages and, over 80% of participants responded that they would not add additional elements to any of the 5 HITs™. Conclusion: The AART-HIT™ methodology, including the FAS™ were comprehensive enough for clinical use in providing a personalised treatment plan for individual patients.

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.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.291
GPT teacher head0.448
Teacher spread0.157 · 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