Investigating the Efficacy of Layered Moderate Tension Reduction Suturing in Facial Aesthetic Surgery
Bibliographic record
Abstract
OBJECTIVE: This study aims to investigate the efficacy of the layer-by-layer moderate tension reduction suture technique in head and facial aesthetic plastic surgery. METHODS: A retrospective analysis was performed on the clinical data of 80 patients who underwent head and facial cosmetic and plastic surgery in the outpatient department of our hospital from April 2022 to April 2024. Among these, the experimental group received the layer-by-layer moderate tension reduction suture technique, whereas the control group received the traditional suture technique. The incidence of surgical complications, scar width, and scar quality metrics derived from the Patient and Observer Scar Assessment Scale (POSAS) and Vancouver Scar Scale (VSS) scores were compared between the two groups. RESULTS: The experimental group had a longer operation time but no complications (0%), compared to the control group's 17.5% complication rate. The χ² test confirmed the experimental group's significantly lower complication rate (P < 0.05). At one, three, six, and 12 months postoperatively, the experimental group had significantly smaller scar widths, lower POSAS scores, and lower VSS scores compared to the control group (all P < 0.05). CONCLUSION: The layer-by-layer moderate tension reduction suture technique demonstrated substantial advantages in head and facial aesthetic plastic surgery. It effectively minimized surgical complications, reduced scar width, and enhanced patients' scar appearance scores, making it highly worthy of clinical 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 | 0.002 | 0.003 |
| 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 | 0.001 |
| 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".