Exploring Reliability of Scar Rating Scales Using Photographs of Burns From Children Aged up to 15 Years
Bibliographic record
Abstract
Assessing burn scars from photographs is a common practice given the growing trend to support health service delivery via electronic media (eg, email, videoconferencing). Scar rating scales, originally designed for in-person assessment, have been used to rate scars from photographic images. Evidence for the reliability of this practice is lacking. Five raters completed three scar rating scales (Patient and Observer Scar Scale, Manchester Scar Scale, modified Vancouver Scar Scale), both in-person and using photographs on 12 participants (seven male, five female) with 18 scar areas (3 × 3 cm). Interrater reliability for the scar parameters of vascularity, color, contour, pliability, and overall opinion achieved intraclass correlation coefficient values of between 0.71 and 0.87 (in-person) and 0.72 and 0.77 (using photographs) for multiple raters. The level of agreement between in-person and photographic assessment was below acceptable levels, which brings into question construct validity when scar rating scales are used in a way for which they were not designed. Reliability estimates in this study were likely reduced by the underrepresentation of scars in the more severe range. This limitation needs to be addressed in future research. Advances are required in the development and refinement of burn scar rating scales, specifically for photographic use, given their routine use in clinical care.
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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 | 0.033 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".