Extending the Range of Measurement for Minimally Invasive Treatments by Adding New Concepts to FACE-Q Aesthetics Scales
Bibliographic record
Abstract
Background: The Satisfaction with Face Overall and Psychological Function scales are the most frequently used FACE-Q Aesthetics module scales. This study aimed to extend their range of measurement by adding and testing new concepts. We aimed to create FACE-Q Aesthetics item libraries. Methods: In-depth concept elicitation interviews were conducted. Concepts were formed into items and refined through multiple rounds of patient and expert input. The items were tested with people living in the United States, Canada, and the United Kingdom who had minimally invasive facial aesthetic treatments. Participants were recruited through an online platform (ie, Prolific). Psychometric properties were examined using Rasch measurement theory analysis, test-retest reliability, and construct validity. Results: We conducted 26 interviews. New concepts were developed into items and refined with input from 12 experts, 11 clinic patients, and 184 Prolific participants. A sample of 1369 Prolific participants completed 52 appearance and 22 psychological items. After removing 10 and 2 items respectively, the psychometric tests provided evidence of reliability with the person separation index, Cronbach alpha, and test-retest reliability values without extremes of 0.88 or more. For validity, lower scores were associated with looking older than one's age, being more bothered by facial skin laxity, treatment wearing off, and having deeper lines on Merz Assessment scales. Short-form scales formed from the 42 appearance items provide examples of item library application. Conclusions: This study provides an innovative means to customize scales to measure appearance and psychological function that maximizes content validity and minimizes respondent burden in the context of minimally invasive treatments.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".