Psychometric Findings for the SCAR-Q Patient-Reported Outcome Measure Based on 731 Children and Adults with Surgical, Traumatic, and Burn Scars from Four Countries
Bibliographic record
Abstract
BACKGROUND: Each year, millions of individuals develop scars secondary to surgery, trauma, and/or burns. Scar-specific patient-reported outcome measures to evaluate outcomes are needed. To address the gap in available measures, the SCAR-Q was developed following international guidelines for patient-reported outcome measure development. This study field tested the SCAR-Q and examined its psychometric properties. METHODS: Patients aged 8 years and older with a surgical, traumatic, and/or burn scar anywhere on their face or body were recruited between March of 2017 and April of 2018 at seven hospitals in four countries. Participants answered demographic and scar questions, the Fitzpatrick Skin Typing Questionnaire, the Patient and Observer Scar Assessment Scale (POSAS), and the SCAR-Q. Rasch measurement theory was used for the psychometric analysis. Cronbach's alpha, test-retest reliability, and concurrent validity were also examined. RESULTS: Consent was obtained from 773 patients, and 731 completed the study. Participants were aged 8 to 88 years, and 354 had surgical, 184 had burn, and 199 had traumatic scars. Analysis led to refinement of the SCAR-Q Appearance, Symptoms, and Psychosocial Impact scales. Reliability was high, with person separation index values of 0.91, 0.81, and 0.79; Cronbach alpha values of 0.96, 0.91, and 0.95; and intraclass correlation coefficient values of 0.92, 0.94, and 0.88, respectively. As predicted, correlations between POSAS scores and the Appearance and Symptom scales were higher than those between POSAS and Psychosocial Impact scale scores. CONCLUSIONS: With increasing scar revisions, a scar-specific patient-reported outcome measure is needed to measure outcomes that matter to patients from their perspective. The SCAR-Q represents a rigorously developed, internationally applicable patient-reported outcome measure that can be used to evaluate scars in research, clinical care, and quality improvement initiatives.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".