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 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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".