Rating the resolving hypertrophic scar: Comparison of the Vancouver Scar Scale and scar volume
Bibliographic record
Abstract
The increased focus of research interests and clinical documentation on outcomes demands that evaluation tools provide reliable and valid data. The Vancouver Scar Scale (VSS) was developed to provide a more objective measurement of burn scars; however, the validity (a test's ability to measure the phenomenon for which it was designed) of the VSS has not been tested. To examine the construct validity of the VSS, we compared it with scar volume, which has established face validity. Burn scars were evaluated monthly for a minimum of 7 months. Three scar volume measurements were performed on each scar. In addition, 3 independent examiners completed the VSS for the same scar. The data generated by these 2 measurements were used to establish the following: (1) the interrater agreement estimated by interclass correlation coefficient, (2) convergence validity, (3) the sensitivity of the assessments to discriminate changes in the scar over time, and (4) the prevalence of related parameters that are not currently being captured by the VSS. In an attempt to address some of the deficiencies of the VSS, we propose several modifications. We anticipate that these changes will increase the reliability and validity of the VSS through an increase in the awareness that training in the use of this scale is required, through improvement in the quality of the subscales, and through the documentation of additional pertinent information.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".