Measuring outcomes for temple hollowing treatment: Content validity of new and existing FACE‐Q scales
Bibliographic record
Abstract
BACKGROUND: The FACE-Q, a validated, modular patient-reported outcome measure with global uptake, currently does not have a scale to measure the appearance of the temples. Objectives of our study were to develop a new FACE-Q scale for appearance of temples and assess content validity of two existing FACE-Q scales in the context of temple hollowing: Satisfaction with Facial Appearance and Psychological Function. METHODS: A heterogeneous sample of adults who were seeking or had received treatments for temple hollowing was recruited from three outpatient clinics in the United States. Semi-structured interviews using an interpretive description approach were completed to elicit concepts and generate an item pool and assess content validity of the two existing FACE-Q scales. The item pool data were used to develop preliminary Temple scale, which was refined based on patient and expert feedback. RESULTS: Participants (N = 15, 55 ± 9 years) described a range of esthetic concerns related to temple hollowing and its treatment. The data were used to draft the FACE-Q Satisfaction with Temples scale, which was refined through input from patients (N = 12) and clinicians (N = 5), resulting in a 16-item FACE-Q Satisfaction with Temples scale. The scale covers concepts of fullness, harmony, scenarios (eg, mirror, bright lights), age, and shape. Content validity of the two existing FACE-Q scales was substantiated. CONCLUSION: The FACE-Q Satisfaction with Temples scale fills an important gap in patient-reported outcome measurement in facial esthetics. The scale will be field-tested to finalize content and develop the scoring algorithm prior to implementation in clinical practice and research.
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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 | 0.037 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".