Applicability of Patient-Reported Outcome Measures to Aesthetic Medicine Patient Archetypes
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures (PROMs) are valuable in understanding patient motivations, setting expectations, and ensuring satisfaction. As the aesthetic industry expands globally, factors that motivate the treatment goals of the aesthetic patient reflect evolving social, cultural, and commercial influences. OBJECTIVE: This article will assess the applicability of current PROMs used in aesthetic medicine to an increasingly diverse patient population and consider their ability to measure the underlying motives that drive different types of patients to pursue their specific goals. METHODS: PubMed database was searched for studies using PROMs to evaluate the motivations and expectations of aesthetic patients. RESULTS: Seven validated aesthetic PROM tools were reviewed against a backdrop of different patient segments as represented by the 4 patient archetypes: Positive Aging, Beautification, Correction, and Transformation. None of the tools could universally represent the individual motivations and expectations of all 4 patient archetypes. CONCLUSION: There is a need for updated PROMs in aesthetics that are applicable to patients with different motivations or expected outcomes than the traditional rejuvenation patient. PROMs that help decode patient motivations and that are developed with more diverse patient involvement will help aesthetic clinicians better understand the goals and expectations of new patient segments.
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 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 | 0.143 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".