Establishing test-retest reliability and the smallest detectable change of FACE-Q Aesthetic Module scales
Bibliographic record
Abstract
Background The test-retest (TRT) reliability of FACE-Q Aesthetic scales is yet to be assessed. The aim of this study was to establish the TRT reliability of 17 FACE-Q Aesthetic scales and determine the smallest detectable change (SDC) that can be identified using these scales. Methods Data were collected from an online international sample platform (Prolific). Participants ≥20 years old, who had been to a dermatologist or plastic surgeon for a facial aesthetic treatment within the past 12 months were asked to provide demographic and clinical information and complete an online REDcap survey consisting of 17 FACE-Q Aesthetic scales. Participants were asked if they would be willing to complete the survey again in 7 days. Only the participants who reported no important change in the scale construct and completed the retest within 14 days were included. Results A total of 342 unique participants completed the TRT survey. The mean age of the sample was 36.6 (±11.5) years, and 82.4% were female. With outlier data removed, all FACE-Q scales demonstrated an intraclass correlation coefficient >0.70 indicating "good" TRT reliability. The standard error of measurement for the included scales ranged from 3.37 to 11.87, corresponding to a range of SDC group from 0.95 to 3.23 and SDC ind from 9.34 to 32.91. Conclusion All included FACE-Q scales demonstrated sufficient TRT reliability and stability overall after the outlier data were removed. Moreover, the authors calculated the values for the SDC for these scales.
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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 | 0.020 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".