The BODY-Q Stretch Marks Scale: A Development and Validation Study
Bibliographic record
Abstract
BACKGROUND: Stretch marks are common permanent dermal lesions that can cause psychosocial distress. A number of treatment modalities are available, with the majority targeted towards collagen production. OBJECTIVES: To develop and field test a new BODY-Q scale to measure appearance of stretch marks in order to provide a means to incorporate the patient perspective into future treatment studies. METHODS: We previously described the development of the BODY-Q conceptual framework, which involved a literature review, 63 patient interviews, 22 cognitive interviews and input from 9 experts, and the international field-test study that involved 403 weight loss and 331 body contouring patients. To develop the Stretch Marks scale, we reexamined appearance codes from the original interviews. The scale was field tested in an international study. Rasch measurement theory (RMT) analysis was used to refine the scale and examine measurement properties. RESULTS: The Stretch Marks scale was completed by 630 participants, who provided 774 assessments. After dropping 3 items, the data fit the Rasch model (P = 0.56). Items (eg, length, width, amount, location, up close) mapped out a well-targeted clinical hierarchy. All items had ordered thresholds and good item fit. There was no evidence of differential item functioning (bias) by gender, age group or language (English vs Danish). The scale evidenced high reliability (ie, person separation index = 0.94, Cronbach's alpha = 0.97). For construct validity, the mean score correlated with the total number of body areas with stretch marks, higher BMI before bariatric surgery, and other BODY-Q scales. CONCLUSIONS: This scale could be used to measure the impact of innovative treatments for stretch marks.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".