Measuring Satisfaction with Minimally Invasive Aesthetic Treatments with the SKIN-Q Treatment Outcome Scale
Bibliographic record
Abstract
BACKGROUND: A key outcome in aesthetic treatments is the patient's view of how their skin looks and feels after a treatment. OBJECTIVES: The aim of this study was to add a Treatment Outcome scale to the SKIN-Q patient-reported outcome measure. METHODS: Concept elicitation interviews were performed with patients in Canada and the United States. Data were coded, analyzed, and used to draft a Treatment Outcome scale. The scale was refined with patient and expert feedback and field tested in an online sample (ie, Prolific). Psychometric analyses were performed to examine reliability and validity. RESULTS: The concept elicitation interviews included 26 participants. The first draft of the Treatment Outcome scale included 32 items that assessed changes in appearance (eg, look better) and well-being (eg, feel more confident). Items were revised with input from 12 experts, 11 patients, and 174 online participants who had aesthetic face and/or body treatments and provided 180 survey responses, resulting in 36 items. Prolific data from 499 participants provided 542 assessments. The sample comprised 80.6% women; 78.8% had a facial treatment, 11.4% had a body treatment, and 9.8% had both. Data for a final 10-item Treatment Outcome scale fit the Rasch model (chi-square = 50.46, df = 40, P = .124). The scale evidenced high reliability, with the person separation index, Cronbach α, and intraclass correlation coefficent values ≥.87. A total of 19 out of 22 (86%) predefined construct validation hypotheses were accepted. CONCLUSIONS: This new SKIN-Q scale can be used alongside other patient-centered outcome tools to measure outcomes of minimally invasive aesthetic treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".