Development and Validation of the Jawline Subject Satisfaction Scale
Bibliographic record
Abstract
Introduction: Research in aesthetic medicine commonly includes evaluations of subject satisfaction with treatment results. However, conventional analytic methods typically generate statistically imprecise ordinal scores. To overcome this limitation, researchers have begun employing the Rasch model, an analytical framework grounded in item response theory. The Rasch model permits scale modifications capable of enhancing measurement accuracy. This study focuses on using the Rasch model to evaluate a scale measuring subject satisfaction following aesthetic treatments to the jawline. Objective: To develop and validate a multiitem, self-administered questionnaire measuring patient satisfaction with aesthetic treatment of the jawline. Methods: A 10-item questionnaire [The Jawline Subject Satisfaction Scale (JS 3 )] was devised to measure subject satisfaction following aesthetic treatments of the jawline. Each question was responded to using a 5-point Likert scale, with response selections ranging from “very much satisfied” to “very much dissatisfied” or “strongly agree” to “strongly disagree.” The scale's psychometric properties (reliability and separation for items and persons, item and person fit statistics, and unidimensionality and local independence) were validated using a Rasch model based on a dataset collected from a sample of forty subjects. Results: The results of the Rasch analysis revealed high internal consistency of the JS 3 , with a person reliability estimate of 0.86 and an item reliability estimate of 0.96. The separation estimates for persons and items were 2.50 and 4.72, respectively, demonstrating the scale's ability to differentiate between high and low responders and validating the instrument's construct. All infit and outfit values fell within the established range (0.5-1.5), and the data fit the model of unidimensionality and local independence. Raw score transformations into logits were conducted, which were then converted to Rasch measurements. These measurements are available for use in practice for conducting standard statistical analyses evaluating treatment and/or group effects. Conclusions: The application of the Rasch model produced a valid and reliable scale (ie, JS 3 ) for measuring satisfaction with the appearance of the jawline following aesthetic 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 | 0.019 | 0.021 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".