Determining the Independent Risk Factors for Worse SCAR-Q Scores and Future Scar Revision Surgery
Bibliographic record
Abstract
BACKGROUND: Scar revisions have been increasing in number. Patient-reported outcome measures are one tool to aid scar modulation decision-making. The aims of this study were to determine patient, scar, and clinical risk factors for (1) low SCAR-Q Appearance, Symptom, and Psychosocial Impact scores and how this differs for children; and (2) the potential need for future scar revision surgery to better identify such patients in a clinical setting. METHODS: A multicenter international cross-sectional cohort study based on survey data of participants with traumatic, surgical, and burn scars attending plastic, hand, and burn clinics in four countries was conducted following the Strengthening the Reporting of Observational Studies in Epidemiology checklist. Univariate analysis to identify risk factors and multivariable logistic analysis to select risk factors were completed. Collinearity for nonindependent factors and C statistic for model discrimination were also calculated. RESULTS: Seven hundred thirty-one participants completed the study booklet, and 546 participants (74.7 percent) had full data. Independent risk factors were determined to be a bothersome scar and perception of scarring badly for all three scales. Risk factors for self-reporting the need for future surgery included a health condition, scarring badly, scar diagnosis, prior scar revision, and low Psychosocial Impact scores. We did not identify evidence of multicollinearity. C statistics were high (0.81 to 0.84). CONCLUSIONS: This study is the first multicenter international study to examine independent risk factors for low patient-reported outcome measure scores and the potential need for future scar revision surgery. Patients that perceive themselves as scarring badly and having a bothersome scar were at a higher risk of scar appearance concern, an increased symptom burden, and poorer psychosocial impact scores. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".