Development of methods and tools based on reverse engineering for pediatric burn scar diagnosis and treatment
Bibliographic record
Abstract
The treatment of pediatric burns plays a key role in the healing process and long-term recovery. One of the main challenges is monitoring the scar over time and adjusting the therapy based on the patient’s specific needs. Today, scar evaluation mostly relies on subjective assessment scales like the Vancouver Scar Scale (VSS) and the Patient and Observer Scar Assessment Scale (POSAS). However, these methods often lead to inconsistent results due to differences in how doctors interpret the same scar, sometimes resulting in less-than-optimal treatment choices. This thesis explores a more objective way to evaluate the condition of scarred skin, using engineering approaches inspired by reverse engineering. In particular, it focuses on two main parameters from the traditional scales: skin surface roughness and pliability. The work outlines the full process—from the initial literature review, to the design and implementation of evaluation methods, and the development of tools or software prototypes. The thesis also includes the testing phase of these methods, with results reviewed and validated by medical professionals. Finally, it discusses how these tools could be used in clinical settings and highlights future directions to further improve the system.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".