How to assess scar hypertrophy—a comparison of subjective scales and Spectrocutometry: A new objective method
Bibliographic record
Abstract
Scar hypertrophy is a significant clinical problem involving both linear scars from elective surgery and scars caused by trauma or burns. The treatment of hypertrophic scars is often time consuming, and patients may need to be followed up for months or even years. The methods for reliable quantification of scar hypertrophy are at present unsatisfying. We have developed a new, objective method, Spectrocutometry, for documentation and quantification of scar hypertrophy. The instrument is based on standardized digital imaging and spectral modeling and calculates the estimated concentration change of hemoglobin and melanin from the entire scar and also provides standardized images for documentation. Three plastic surgeons have assessed 37 scars from melanoma surgery using Spectrocutometry, the Vancouver scar scale, and the patient and observer scar assessment scale. The intraclass correlation coefficient for the Vancouver scar scale and the patient and observer scar assessment scale was lower than required for reliable assessment (r=0.66 and 0.60, respectively). The intraclass correlation coefficient for Spectrocutometry was high (r=0.89 and 0.88). A Bayesian network analysis revealed a strong dependency between the estimated concentration change of hemoglobin and scar pain. Spectrocutometry is a feasible method for measuring scar hypertrophy. It is shown to be more reliable than subjective rating in assessing linear surgical scars.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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; 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".