The Importance of a Three-dimensional-Based Approach With Personalized Models for Accurately Assessing TBSA
Bibliographic record
Abstract
In this article, the relevance of a new three-dimensional computer-Based computer-based framework with personalized 3D models for accurately assessing the TBSA is demonstrated through preliminary results and validation studies. First, a 3D rendering interface was developed for representation and calculation of TBSA. The personalized 3D models were built from anthropometric measurements using MakeHuman software. Fifteen paired models were randomly built with an equal BSA but a different morphology. The difference of local BSA, TBSA burned, and Parkland fluids between each paired models were observed to highlight the impact of morphology's variation on the TBSA. Finally, a preliminary validation study was made on 4 mannequins by 14 volunteers to assess the accuracy of the 3D models built with MakeHuman software and TBSA burned assessment with the proposed method. Small variations in the morphology impacted the TBSA assessment. Mannequin's 3D models built with MakeHuman software presented an absolute error of 3 ± 2.2 % with no significant difference with their scans. The proposed approach allows for a better assessment of TBSA with a lower variability. No significant difference in the scores for expert and nonexpert conditions was observed. Personalized 3D model to the patient's morphology is suggested to overcome the difficulty of patients with specific morphologies such as obese and children. The proposed framework appears to be relevant for personalizing and accurately assessing TBSA and could reduce morbidity and mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".