FACE-Q Eye Module for Measuring Patient-Reported Outcomes Following Cosmetic Eye Treatments
Bibliographic record
Abstract
IMPORTANCE: Aesthetic eye treatments can dramatically change a person's appearance, but outcomes are rarely measured from the patient perspective. The patient perspective could be measured using an eye-specific patient-reported outcome measure. OBJECTIVE: To describe the development and psychometric evaluation of FACE-Q scales and an adverse effect checklist designed to measure outcomes following cosmetic eye treatments. DESIGN, SETTING, AND PARTICIPANTS: Pretreatment and posttreatment patients 18 years and older who had undergone facial aesthetic procedures were recruited from plastic surgery clinics in United States and Canada and completed FACE-Q scales between June 6, 2010, and July 14, 2014. We used Rasch Measurement Theory, a modern psychometric approach, to refine the scales and to examine psychometric properties. MAIN OUTCOMES AND MEASURES: The FACE-Q Eye Module, which has 4 scales that measure appearance of the eyes, upper and lower eyelids, and eyelashes. Scale scores range from 0 (worst) to 100 (best). The module also includes a checklist measuring postblepharoplasty adverse effects. RESULTS: Overall, 233 patients (81% response rate) 18 years and older participated. Adverse effects included being bothered by eyelid scars, dry eyes, and eye irritation. In Rasch Measurement Theory analysis, each scale's items had ordered thresholds and good item fit. Person Separation Index and Cronbach α were greater than or equal to 0.83. Higher scores on the eye scales correlated with fewer adverse effects (range, -0.26 to -0.36). In the pretreatment group, older age correlated with lower scores (range, -0.42 to -0.51) on the scales measure appearance of the eyes and upper and lower eyelids. Compared with the pretreatment group, posttreatment participants reported significantly better scores on the scales measuring appearance of eyes overall, as well as upper and lower eyelids. CONCLUSIONS AND RELEVANCE: The FACE-Q Eye Module can be used in clinical practice, research and quality improvement to collect evidence-based outcomes data. LEVEL OF EVIDENCE: NA.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".