Application of a Chest Compression Device Combined with Extended Self-Care for Scar Prevention in Patients After Keloid Excision Surgery
Bibliographic record
Abstract
Objective: To explore the effectiveness of a chest compression device combined with extended self-care for scar prevention in patients following keloid excision surgery. Methods: Forty patients (36 lesions) who underwent keloid excision surgery at the Department of Plastic and Reconstructive Surgery, First Medical Center, PLA General Hospital from June 2022 to June 2024 were selected. They were randomly divided into an experimental group and a control group, with 20 patients in each group. The control group received traditional elastic garment compression therapy, while the experimental group used a chest compression device designed for scar prevention. Scar width, hypertrophy, and Vancouver Scar Scale (VSS) scores were compared between the two groups at 6 months post-operation. Results: There were no statistically significant differences between the two groups in terms of gender, age, disease duration, lesion area, or location (P > 0.05). However, VSS scores (except for pliability) in the experimental group were significantly lower than those in the control group (P < 0.05). Conclusion: The chest compression device for scar prevention is more effective than traditional elastic garments in preventing scar hypertrophy after chest wall keloid excision surgery, and it has high clinical value, making it worthy of promotion.
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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 | 0.001 |
| 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) | 0.001 | 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".