The SKIN-Q: An Innovative Patient-Reported Outcome Measure for Evaluating Minimally Invasive Skin Treatments for the Face and Body
Bibliographic record
Abstract
Background: As the aesthetics field continues to innovate, it is important that outcomes are carefully evaluated. Objectives: To develop item libraries to measure how skin looks and feels from the patient perspective, that is, SKIN-Q. Methods: Concept elicitation interviews were conducted and data were used to draft the SKIN-Q, which was refined with patient and expert feedback. An online sample (i.e., Prolific) provided field-test data. Results: We conducted 26 qualitative interviews (88% women; 65% ≥ 40 years of age). A draft of the SKIN-Q item libraries were formed and revised with input from 12 experts, 11 patients, and 174 online participants who provided 180 survey responses. The psychometric sample of 657 participants (82% women; 36% aged ≥40 years) provided 713 completed surveys (facial, n = 595; body, n = 118). After removing 14 items, the psychometric analysis provided evidence of reliability (≥0.85) and validity for a 20-item set that measures how skin feels and a 46-item set that measures how skin looks. Short-form scales were tested to provide examples for how to utilize the item sets. Conclusion: The SKIN-Q represents an innovative way to measure satisfaction with skin (face and body) in the context of minimally invasive treatments.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".