Danish translation, linguistic validation, and cultural adaption of SCAR-Q
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it