Investigating the intra- and inter-rater reliability of a panel of subjective and objective burn scar measurement tools
Bibliographic record
Abstract
BACKGROUND: Research into the treatment of hypertrophic burn scar is hampered by the variability and subjectivity of existing outcome measures. This study aims to measure the inter- and intra-rater reliability of a panel of subjective and objective burn scar measurement tools. METHODS: Three independent assessors evaluated 55 scar and normal skin sites using subjective (modified Vancouver Scar Scale [mVSS] & Patient and Observer Scar Assessment Scale [POSAS]) and objective tools. The intra-class correlation coefficient was utilised to measure reliability (acceptable when >0.70). Patient satisfaction with the different tools and scar parameter importance were assessed via questionnaires. RESULTS: The inter-rater reliabilities of the mVSS and POSAS were below the acceptable limit. For erythema and pigmentation, all of the Scanoskin and DSM II measures (except the b* value) had acceptable to excellent intra and inter-rater reliability. The Dermascan ultrasound (dermal thickness, intensity) had excellent intra- and inter-rater reliability (>0.90). The Cutometer R0 (firmness) had acceptable reliability but not R2 (gross elasticity). All objective measurement tools had good overall satisfaction scores. Patients rated scar related pain and itch as more important compared to appearance although this finding was not sustained when corrected for multiple comparisons. CONCLUSION: The objective scar measures demonstrated acceptable to excellent intra- and inter-rater reliability and performed better than the subjective scar scales.
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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 | 0.062 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".