Psychometric validation of the FACE‐Q Aesthetics Satisfaction with Temples scale in individuals seeking minimally invasive treatment to improve temple hollowing
Bibliographic record
Abstract
BACKGROUND: The FACE-Q Aesthetics is used extensively to measure patient reported outcomes for minimally invasive and surgical facial aesthetic treatments. We recently developed a new FACE-Q scale to assess satisfaction with the appearance of the temples. AIM: The aim of this study was to field test the FACE-Q Satisfaction with Temples scale to examine its psychometric properties. METHODS: The FACE-Q Satisfaction with Temples scale was administered to 171 adults (22 years or older) seeking minimally invasive treatment to improve temple hollowing as part of a clinical trial. The severity of temple hollowing was established through the clinician-reported Allergan Temple Hollowing scale (clinician-rated). The psychometric properties of the FACE-Q Satisfaction with Temples scale were established by testing Rasch Measurement Theory (RMT) assumptions and model fit; unidimensionality by principal component analysis; and construct validity by hypothesis testing. RESULTS: = 20.47, df = 24, p = 0.67), all items had ordered thresholds, and good item fit. Scale reliability was high, with Person Separation Index and Cronbach alpha values with and without extremes ≥0.93. Principal component analysis revealed a single component with high factor coefficients. Construct validity was established as scores for the Satisfaction with Temples and Face Overall scales were correlated (r = 0.623, p < 0.001). CONCLUSION: The FACE-Q Satisfaction with Temples scale is a reliable and valid measure that can be used in clinical practice and research to measure outcomes following treatment for temple hollowing.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".