Smart Scar Care-Industry 4.0 in Individualized Compression Garments: A Randomized Controlled Crossover Feasibility Study.
Bibliographic record
Abstract
BACKGROUND: We tested the workflow and comparability of compression garments (CG) automatically knitted from 3D-body-scan data (3DBSD) versus manually measured data for scar treatment. Industry 4.0 has found its way into surgery, enhancing the trend toward personalized medicine, which plays an increasingly important role in CG scar therapy. Therefore, we conducted a study to evaluate the workflow from 3DBSD to fast and precisely knitted CG and compared it with standard of care. METHODS: A randomized controlled crossover feasibility study was conducted as part of the individual medical technology research project "Smart Scar Care." Objective and patient-reported outcome measures were documented for 10 patients with hypertrophic burn scars at baseline and after wearing CG automatically knitted from 3DBSD versus CG from manually measured data for one month. RESULTS: The "scan-to-knit" workflow and the study design were feasible in 10 of 10 patients. No adverse effects were found. 3DBSD showed a bias of half a centimeter compared with manually measured data and wider limits of agreement. With respect to fit, comfort, suitability, Vancouver Scar Scale, Patient and Observer Scar Assessment Scale, stiffness and microcirculation, this was a promising pilot study. Stiffness and blood flow were increased in scars compared with normal skin. The highest rank correlations were found between pain and itch, stiffness and Patient and Observer Scar Assessment Scale, Vancouver Scar Scale, and pain. CONCLUSIONS: These results indicate that automatically knitted CG using 3DBSD could become an alternative to the standard of care, especially with regard to economical and faster patient care. The produced scan data opens the door for objective scar science.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".