Predictive Modeling for Personalized Three-Dimensional Burn Injury Assessments
Bibliographic record
Abstract
For patients with major burn injuries, an accurate burn size estimation is essential to plan appropriate treatment and minimize medical and surgical complications. However, current clinical methods for burn size estimation lack accuracy and reliability. To overcome these limitations, this paper proposes a 3D-based approach-with personalized 3D models from a limited set of anthropometric measurements-to accurately assess the percent TBSA affected by burns. First, a reliability and feasibility study of the anthropometric measuring process was performed to identify clinically relevant measurements. Second, a large representative stratified random sample was generated to output several anthropometric features required for predictive modeling. Machine-learning algorithms assessed the importance and the subsets of anthropometric measurements for predicting the BSA according to specific patient morphological features. Then, the accuracy of both the morphology and BSA of 3D models built from a limited set of measurements was evaluated using error metrics and maximum distances 3D color maps. Results highlighted the height and circumferences of the bust, neck, hips, and waist as the best predictors for BSA. 3D models built from three to four anthropometric measurements showed good accuracy and were geometrically close to gold standard 3D scans. Outcomes of this study aim to decrease medical and surgical complications by decreasing errors in percent TBSA assessments and, therefore, improving patient outcomes by personalizing care.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".