Danish translation, linguistic validation, and cultural adaption of SCAR-Q
Bibliographic record
Abstract
Abstract Background Scars can significantly impact an individual’s physical appearance, emotional well-being, and quality of life. Patient-reported outcome measures are essential for assessing these effects, with SCAR-Q being a validated tool that includes three independently functioning scales. This study aimed to translate, linguistically validate, and culturally adapt SCAR-Q for use in Denmark. Methods SCAR-Q was translated into Danish using a six-step methodology based on the International Society for Pharmacoeconomics and Outcomes Research and the World Health Organization guidelines to ensure accuracy and cultural adaptation. The process included six steps: (1) preparation, (2) forward translations, (3) back translation, (4) expert panel review, (5) cognitive debriefing interviews with scar patients, and (6) final proofreading by experienced clinicians. Results The forward translation resulted in a harmonized Danish version with minor discrepancies between the two translations that primarily involved terminology adjustments for words such as “contour,” “noticeable,” “flaky,” and “tingly.” The back translation confirmed conceptual equivalence, with only four minor refinements required. The expert panel deemed the translated version clinically and linguistically appropriate. Cognitive interviews with 10 patients demonstrated high comprehension, with no concerns identified. The final proofreading led to the final Danish SCAR-Q. Conclusions The Danish version of SCAR-Q was successfully translated, culturally adapted, and validated following a rigorous six-step process. Level of evidence: Not gradable.
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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 | 0.119 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".