Development of a new visual analogue scale for the assessment of area scars.
Bibliographic record
Abstract
OBJECTIVE: Clinical scar assessment lacks standardized methodology and consensus on the most appropriate evaluation instrument. This study empirically evaluated whether area scars could be validly assessed by naive observers with the objective to develop and validate a novel multidimensional visual analogue scale (VAS) for the assessment of area scars. METHODS: Standardized digital photographs of radial forearm free flap (RFFF) donor sites were obtained. Naive observers evaluated the images in three sequential psychophysical experiments, which led to the development of the new scar scale. These experiments involved initial evaluation of four dimensions (pigmentation, vascularity, observer comfort, acceptability) using a paired comparison (PC) paradigm and correlation with ratings of overall severity using a VAS, and initial VAS test phase followed by formal debriefing, and, subsequently, evaluation of a VAS for the four dimensions in addition to contour. Validation involved determination of intra- and interrater reliability and correlational analysis. RESULTS: Across all three experiments, 56 observers evaluated 101 images, generating 12 720 observations for analysis. PC data demonstrated that observers could assess scars with high reliability and internal consistency for all dimensions (> 95%). Overall (VAS) severity correlated highly with all dimensions, including contour. The new VAS yielded high levels of correlation (r = .72-.98, p < .01). CONCLUSION: Comprehensive VAS analysis demonstrates high reliability in mirroring PC results for multiple dimensions of area scars. These data support our novel multidimensional VAS method as a valid, reliable, simple, and time-efficient instrument for clinical and research use. We introduce the Western Scar Index as a new measurement tool with many potential applications.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".