Smart Scar Care Pad: An Innovated Silicone-Based Insert That Improved Severe Hypertrophic Scar by Integrating Optimal Pressure and Occlusion Effects
Bibliographic record
Abstract
Objective: Inserts are typically used in conjunction with pressure garment to improve the outcomes of hypertrophic scar (HS) treatment. However, the types of insert materials currently available have many limitations, thus hindering their therapeutic effect. An innovative insert material, the Smart Scar Care Pad (SSCP), was invented to maximize treatment outcomes through enhanced compression and occlusion. The study aimed to investigate the properties of the SSCP and its efficacy to serve as a suitable insert material for HS management. Approach: The physical properties and biocompatibility of SSCP were examined. A self-controlled clinical study of 45-day intervention using SSCP in comparison with Plastazote was conducted on 25 subjects with HS. Study outcomes included scar parameters measured both subjectively and objectively and patient feedback questionnaire. Results: The SSCP demonstrated equivalent and even superior performance when compared with commercial products in tests of biological safety and physical properties. Both arms demonstrated significant time effects on Vancouver Scar Scale total score (p = 0.000), melanin score (p = 0.042), pliability (p = 0.039), and hydration score (p = 0.013). Significant intervention and time interaction effect was found in the pliability (p = 0.048). From the feedback questionnaire, patients reported that SSCP was more conformable and there was less displacement during movement (p = 0.040) compared with conventional insert. Innovation: The SSCP is the first insert that combined the pressure enhancing effect of a traditional pressure insert and occlusion effect of silicone insert. Conclusion: The SSCP can serve as a safe and effective insert material in HS treatment, especially in improving HS pliability.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".