“I Want It to Look Natural”: Development and Validation of the FACE-Q Aesthetics Natural Module
Bibliographic record
Abstract
BACKGROUND: The concept of "natural" after a facial aesthetic treatment represents an understudied area. We added scales to FACE-Q Aesthetics to provide a means to measure this concept from the patient's perspective. OBJECTIVES: The objective of this study was to develop and validate the FACE-Q Aesthetic Natural module. METHODS: Concept elicitation interviews with people having minimally invasive treatments were conducted to explore the natural concept and develop scales. Patient and expert input refined scale content. An online sample (ie, Prolific) of people who had a facial aesthetic treatment was analyzed with Rasch measurement theory to examine psychometric properties. A test-retest reliability study was performed, and construct validity was examined. RESULTS: Interviews with 26 people were conducted. Three scales were developed and refined with input from 12 experts, 11 patients, and 184 online survey participants. Data from 1358 online participants provided evidence of scale reliability and validity. Reliability was high with person separation index, Cronbach alpha, and intraclass correlation coefficient values without extremes ≥0.82. Tests of construct validity confirmed that the scales functioned as hypothesized. Higher scores on the Expectations scale were associated with how important it was to have a natural look and movement after treatment. In addition, higher scores on the Natural Appearance and Natural Outcome scales correlated with better scores on other FACE-Q Aesthetics scales, and were associated with the face looking and feeling natural and with overall satisfaction with facial appearance. CONCLUSIONS: Many people seeking facial aesthetic treatments want to look natural after treatment. These new FACE-Q Aesthetics scales provide a means to measure the concept of natural from the patient's perspective.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".