Measuring the Impact of Surgical and Non-surgical Facial Cosmetic Interventions Using FACE-Q Aesthetic Module Scales: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The FACE-Q Aesthetic module measures patient-important outcomes following surgical and non-surgical facial cosmetic procedures. Objective: The primary aim of this systematic review was to summarize the pre- to post-intervention mean differences of facial aesthetic interventions that evaluate outcomes using the FACE-Q Face Overall, Psychological, and Social scales. Methods: Ovid Medline, Embase, Cochrane, and Web of Science databases were searched on December 20, 2022 with the assistance of a health-research librarian (CRD42023404238). Studies that examined any surgical or non-surgical facial aesthetic intervention in adult patients and used FACE-Q Aesthetics Face Overall, Psychological, and/or Social scales to measure participants before and after treatment were included for analysis. Results: Of 914 potential articles screened, 35 studies met the inclusion criteria. Most studies evaluated surgical (n = 22, 62.9%) versus non-surgical facial cosmetic interventions (n = 13, 37.1%). Rhinoplasty [37.0 points, 95% CI 24.7-49.3, P < 0.01] demonstrated the largest weighted increase in Face Overall scores, whereas the largest increase in Psychological [67.1 points, 95% CI 62.9–71.3, P < 0.01] and Social [63.9 points, 95% CI 53.2–74.6, P < 0.01] scores was demonstrated by a single study evaluating surgical forehead lifts, respectively. Conclusions: This meta-analysis leverages FACE-Q Aesthetic module scoring to present the expected mean differences in Face Overall, Psychological, and Social scale scores for various surgical and non-surgical facial cosmetic interventions. The findings from this review may be used to indirectly compare interventions and contribute to sample size calculations when planning future studies.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".