Establishing Convergent Validity of the FACE-Q Aesthetics Module Scales
Bibliographic record
Abstract
BACKGROUND: FACE-Q Aesthetics scales can be used to assess patient-important outcomes following both surgical and nonsurgical facial cosmetic interventions. Convergent validity is the degree to which the scores of one measurement relate to another measuring a similar construct. OBJECTIVES: The aim of this study was to establish the convergent validity of 11 FACE-Q Aesthetics appearance scales vs the MERZ Aesthetics (Raleigh, NC) scales. METHODS: Data were collected from an online international sample of participants aged ≥20 years, who had presented to a dermatologist or plastic surgeon within the previous 12 months for a minimally invasive facial aesthetic treatment. Participants provided demographic and clinical data and completed 11 FACE-Q Aesthetics scales and 12 MERZ Aesthetics scales. Hypotheses regarding the strength of correlations between these scales were generated a priori. Adequate convergent validity was based on the percentage of correct hypotheses (>75%) and/or correlation ≥0.50 with an instrument measuring a similar construct. RESULTS: In total, 1259 participants were included in this survey. The mean [standard deviation] age of the participants was 42.6 [11.9] years old, and most were female (72.5%), Caucasian (76.9%), and living in the United States (49.9%) or the United Kingdom (42.9%). FACE-Q Lines Overall, Lower Face and Jawline, Appraisal of Lines-Forehead/Between Eyebrows/Crow's Feet/Lips/Nasolabial Folds/Marionette, and Lips scales demonstrated adequate convergent validity with patient-reported MERZ Aesthetics scales. The FACE-Q Face Overall and Cheeks scales did not show adequate convergent validity. CONCLUSIONS: This study provides evidence of convergent validity for FACE-Q Aesthetics appearance scales. Establishing the validity of these scales remains an iterative process and further studies comparing the FACE-Q to other related measurement tools are required to strengthen this evidence.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".