Clinical efficacy analysis of cosmetic suture technique combined with tension reducer in the treatment of facial skin trauma
Bibliographic record
Abstract
BACKGROUND: This study aims to observe the clinical efficacy of cosmetic suture technique combined with tension reducer in the treatment of facial skin trauma and provide more sequential treatments for facial skin trauma. METHODS: Sixty patients with facial skin trauma who visited our department from January 2023 to January 2024 were selected as the research subjects. Patients who received cosmetic sutures combined with tension reducers were selected as the observed group (n = 30), while patients who received simple cosmetic sutures were selected as the control group (n = 30). Follow-up at 1, 3, and 6 months after surgery to compare the condition of scar formation (using the Vancouver scar rating scale), scar width, and patient satisfaction between the 2 groups. RESULTS: After 1, 3, and 6 months of follow-up, the total score of Vancouver scar rating scale in the observed group was lower than that in the control group (P < .05); The average postoperative scar width in the observed group was (0.72 ± 0.07 mm), which was narrower than that in the control group (1.03 ± 0.12 mm) (P < .05). The satisfaction rate of patients in the observed group was 93.33%, which was higher than 73.33% in the control group (P < .05). CONCLUSION: The combination of cosmetic sutures and tension reducer in the treatment of facial skin trauma can effectively improve the scar condition, narrow the scar width, and greatly improve patient satisfaction. It is worth popularizing in the treatment of facial skin trauma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".