Patient Factors That Impact FACE-Q Aesthetics Outcomes: An Exploratory Cross-sectional Regression Analysis
Bibliographic record
Abstract
BACKGROUND: The FACE-Q Aesthetics is a validated tool for assessing patient-reported outcomes related to surgical and nonsurgical facial aesthetic treatments. Recognizing patient-specific variables that may influence FACE-Q scores is essential to control for potential confounders in research. OBJECTIVES: This study aimed to identify factors that predict FACE-Q Aesthetics scale scores. METHODS: A cross-sectional survey was conducted among an international cohort of participants recruited through the Prolific platform. Participants aged 20 years or older, who had undergone noninvasive facial aesthetic procedures within the past year, were included. Demographic and clinical information was collected, and univariable and multivariable linear regression analyses were employed to assess predictors of FACE-Q Face Overall, Psychological, and Social scale scores. RESULTS: A total of 1259 participants were analyzed, with an average age of 42.6 years (±11.9). The mean scores were 52.4 (±18.3) for the Face Overall scale, 56.5 (±23.7) for the Psychological scale, and 62.7 (±24.0) for the Social scale. Several factors were significantly associated (P < .05) with higher scores, including lower BMI, African American ethnicity, male gender, Fitzpatrick skin Type V, residence in the United States, financial stability, and residual effects of previous aesthetic treatments. Younger participants were more likely to report higher Face Overall scores (P < .05). CONCLUSIONS: This study identified several patient characteristics that predict Face Overall, Psychological, and Social scale scores. These findings offer valuable insights into how patient-specific factors influence outcomes following facial aesthetic procedures and underscore the need to account for these variables in future research using the FACE-Q Aesthetics tool.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".