Assessing Scar Outcomes Using Objective Scar Measurement Tools: An Adjunct to Validated Scar Evaluation Scales
Bibliographic record
Abstract
BACKGROUND: The assessment of scar outcomes is important to both patient care and research focused on understanding the results of medical and surgical interventions. The Vancouver Scar Scale (VSS) and Patient and Observer Scar Assessment Scale (POSAS) are validated and simple instruments to assess scars. However, these subjective scales have shortcomings. The VSS fails to capture patient perception and has indeterminate validity and reliability. The POSAS captures patient perception, but the observer scale has been shown to have moderate amounts of interrater variability. Studies highlighting the ability of objective scar assessment tools to produce reliable and reproducible results are needed. In this study, the authors aimed to validate the use of the FibroMeter, ElastiMeter, and SkinColorCatch as objective adjuncts in the assessment of hypertrophic scar and keloid outcomes. METHODS: In this prospective single-center study, scars were assessed using the VSS, the POSAS, and the objective study tools (FibroMeter, ElastiMeter, and SkinColorCatch). Correlations between the different methods of scar assessment were measured. RESULTS: The FibroMeter and SkinColorCatch showed significant correlations with the VSS total and the observer POSAS total. The ElastiMeter showed significant correlations with both the patient and observer POSAS totals. Unexpected correlations between ElastiMeter measurements and the vascularity or pigmentation of scars indicate that scoring of these categories may be influenced by how severe the scar looks to the observer subjectively, underscoring the need for reliable objective scar assessment tools. CONCLUSION: The results highlight the ability of the FibroMeter, ElastiMeter, and SkinColorCatch to assess scars, and demonstrate their potential in serving as important adjuncts to previously validated scar assessment 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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".