Effect of a Chest Compression Device for Scar Prevention Combined with Nurse-Patient WeChat Group on Scar Formation after Keloid Excision
Bibliographic record
Abstract
Objective: To investigate the effect of a chest compression device for scar prevention combined with a nurse-patient WeChat group on scar formation after keloid excision. Methods: Forty patients with chest wall keloids who underwent keloid excision surgery at the Department of Plastic and Reconstructive Surgery, First Medical Center of PLA General Hospital from June 2022 to June 2024 were selected. They were randomly divided into two groups: the observation group (20 cases) and the control group (20 cases). Both groups underwent routine keloid excision, followed by compression therapy for 6 months. The observation group used a chest compression device, while the control group used a compression garment. Scar width, hypertrophy, and Vancouver Scar Scale (VSS) scores were compared between the two groups. Results: There were no significant differences between the two groups in terms of gender, age, disease course, lesion area, and lesion site (P > 0.05). The overall effective rate in the observation group was 95.00%, significantly higher than the 65.00% in the control group, with a statistically significant difference (P < 0.05). After a 6-month follow-up, all VSS indicators (except for pliability) in the observation group (using the chest compression device) were significantly lower than those in the control group (P < 0.05). Conclusion: Compared to the traditional compression garment, the chest compression device for scar prevention is more effective in preventing scar hypertrophy after chest wall keloid excision and improving the appearance of scars. It is worth promoting for clinical application.
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".