A 360° Approach to Patient Care in Aesthetic Facial Rejuvenation
Bibliographic record
Abstract
Background: Aesthetic medicine has traditionally focused on addressing perceived problem areas, with lack of long-term planning and engagement. Objectives: This article describes a patient-centric model for nonsurgical aesthetic medical practice, termed the 360° approach to facial aesthetic rejuvenation. Methods: The 360° approach was divided into 4 foundational pillars. Medical literature, the authors' clinical experiences, and results from patient satisfaction surveys were used to support the approach. Results: Pillar 1 describes the development of a complete understanding of the patient, based on the use of active listening principles, to characterize the patient's current aesthetic concerns, lifestyle, medical and treatment history, treatment goals, attitude toward aesthetic treatment, and financial resources. Pillar 2 involves conducting a comprehensive facial assessment in contrast to a feature-specific assessment, considering multiple facial tissues and structures and their interrelationships, thus helping to prevent the unanticipated consequences of narrowly focused treatment. Pillar 3 describes leveraging all available treatments and techniques in the development of an initial treatment plan arising from the facial assessment. Pillar 4 adds a time dimension to treatment planning, working toward the goal of a long-term modifiable treatment timeline, with full patient support and involvement; this is designed to facilitate a durable, sustained relationship between the patient and aesthetic healthcare professional (HCP). Conclusions: Although implementation involves substantial commitment and time, the patient-oriented focus of the 360° approach can help achieve optimal patient outcomes and the development of enduring patient-HCP relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".