Comparison of Clinical Estimation and Stereophotogrammic Instrumented Imaging of Burn Scar Height and Volume
Bibliographic record
Abstract
Descriptive clinical tools for characterizing burn scars are limited by between-user variability and unknown sensitivity to change over time. We previously described preclinical assessment of stereophotogrammetry as a valid measure of burn-related scars. Here, we compared the estimated vs. instrumented measurements of maximum height and total positive volume of 26 burn scars. The burn scars were imaged with the QuantifiCare LifeViz Micro 3D camera. Three experienced wound care therapists first estimated, then measured using 3D Track software, the imaged scars’ height and volume. Two-factor analysis without replication was performed to calculate intraclass correlation coefficients (ICCs) between assessors’ estimated scar height and volume, and measured height and volume. Two-sided Wilcoxon tests were performed comparing the mean estimated height and volume with the estimated and measured outputs. The estimated scar height’s ICC was 0.595, and for volume, it was 0.531. The measured scar height’s ICC was 0.933 and for volume, it was 0.890. The estimated and measured volume were significantly different (z = −2.87, p = 0.041), while the estimated and measured height were not (z = −1.39, p = 0.161). Stereophotogrammic measurement of scar height and volume is more reliable than clinical photograph assessment. Stereophotogrammetry should be utilized when assessing burn scar height and volume, rather than subjective estimates from clinical scar tools.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".