Turn Your AART into a HIT Using a Complete Range of Aesthetic Injectables: Methodology for Combining Products to Maximise Patient Outcomes
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it