Flexible Pressure Sensors for Optimizing Pressure Garment Therapy in Periarticular Scar Treatment: Preclinical and Clinical Applications
Bibliographic record
Abstract
OBJECTIVE: Pressure garment therapy is a common strategy for controlling hypertrophic scars; however, insufficient pressure due to reduced elasticity or joint movement limits its effectiveness around joints. The FlexiForce B201 pressure sensor offers precise pressure measurements, thereby demonstrating a promising solution. APPROACH: This study used a Bama pig scar model with an untreated group, a pressure garment group, and a pressure monitoring group that was treated with FlexiForce B201 sensors and pressure garments. The therapeutic effects were recorded over 1 month. The clinical research followed the Consolidated Standards of Reporting Trials and was registered as ChiCTR2200064173. Eighty-two patients with peri-joint hypertrophic scars were enrolled. Forty-one patients were randomly assigned to the control group and received conventional pressure garment therapy, whereas the remaining 41 patients were included in the monitoring group. Treatment outcomes were tracked at 3 months and 6 months. RESULTS: The Bama pig scar model demonstrated reduced scar hypertrophy in the monitoring group. In the clinical study, the scar thickness in the monitoring group was 47.76% of the initial thickness after 6 months, thereby representing an additional 11.33% reduction compared to the control group. The Vancouver Scar Scale score of the monitoring group (6.44 ± 1.62) was significantly better than that of the control group (7.33 ± 1.53). INNOVATION: The FlexiForce B201 pressure sensor is soft and flexible. It provides accurate pressure measurements within the pressure garment and guides physicians in adjusting the pressure distribution. CONCLUSION: This study revealed that pressure monitoring technology enhances the effectiveness of pressure garments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".