Evaluating skin colour diversity in the validation of scar assessment tools
Bibliographic record
Abstract
Across scar studies, there is a lack of dark-skinned individuals, who have a predisposition for keloid formation, altered pigmentation and poorer quality of life (QOL). There is a need for patients of colour to be included in scar scale development and validation. In this study, we evaluate the racial diversity of patients included in the validation of scar assessment scales. A systematic review was conducted for articles reporting on the validation of a scar assessment tool. Racial, ethnic and Fitzpatrick skin type (FST) data were extracted. Fifteen scar scale validation studies were included. Nine of the studies did not mention FST, race or ethnicity of the patients. Two of the studies that reported FST or race information only included White patients or included no FST V/VI patients: mapping assessment of scars (MAPS) and University of North Carolina '4P'. Only four studies included non-White patients or dark-skinned patients in the validation of their scar scale: the modified Vancouver Scar Scale (VSS), modified Patient and Observer Scar Assessment Scale (POSAS), acne QOL and SCAR-Q scales. The patients included in the modified VSS validation were 7% and 13% FST V/VI, 14% African in the modified POSAS and 4.5% FST V/VI in the SCAR-Q. We highlight the severe lack of diversity in scar scale validation, with only 4 out of 15 studies including dark-skinned patients. Given the susceptibility of darker-skinned individuals to have poorer scarring outcomes, it is critical to include patients of colour in the very assessment tools that determine their scar prognosis. Inclusion of patients of colour in scar scale development will improve scar assessment and clinical decision-making.
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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 | 0.213 | 0.401 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".