Comparative analysis of the therapeutic effects of cosmetic tension-reducing suturing technique and traditional suturing technique in 120 patients with maxillofacial trauma
Bibliographic record
Abstract
OBJECTIVE: To determine the beneficial effects of cosmetic tension-reducing suturing techniques in repairing maxillofacial trauma. METHODS: A retrospective study was conducted on 120 patients with maxillofacial trauma, who presented to outpatient departments and emergencies from March to September 2024. Patients were divided into two groups equally. The experimental group (n = 60) was subjected to cosmetic tension-reducing suturing. The control group (n = 60) was subjected to traditional suturing. Evaluations targeted clinical outcomes, adverse event rates, patient satisfaction, and scar width and characteristics using the Scar Score- Vancouver Scar Scale (VSS) and Patient and Observer Scar Assessment Scale (POSAS). RESULTS: Longer procedure times were noted in the experimental group, but there was less than 5% adverse event rate compared to 27% of the control group. By the fourth postoperative day, three patients in the experimental group exhibited localized inflammation and infection, which were resolved with secondary cleaning and closure, while all other wounds healed primarily. The experimental group showed a significant improvement of P < 0.05 in aesthetic-functional scores and the scar thickness reduced significantly. CONCLUSION: The application of cosmetic tension-reducing suturing techniques in maxillofacial trauma helps reduce complications and scar formation during the healing period, improves aesthetic outcomes and increases patient satisfaction. Therefore, it enhances the clinical applicability of these techniques.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".