Outcomes of cosmetic suturing for facial trauma in medically underserved counties of western China: A retrospective observational cohort study
Bibliographic record
Abstract
Facial trauma is frequent in medically underserved regions of western China, where lack of access to specialized surgical care contributes to infection, hypertrophic scarring, and psychosocial distress. Although cosmetic suturing is widely used in plastic surgery, its systematic application in rural hospitals has not been well characterized. We conducted a single-center, retrospective, non-comparative observational cohort study at the Qingyang Hospital of Traditional Chinese Medicine from January to December 2024. Sixty consecutive patients (mean age 30.7 ± 13.6 years; 38 males, 22 females) with acute facial lacerations closed by direct approximation were included. Outcomes were evaluated at 6 months using the Vancouver Scar Scale (VSS) and patient satisfaction via the Visual Analog Scale. Logistic regression was performed to identify independent predictors of satisfaction. Falls (46.7%) and road traffic accidents (31.7%) were the leading causes of trauma. Mean scar length was 7.35 ± 5.02 cm, with 23.3% of patients presenting with multi-site lacerations. Superficial infection occurred in 2 cases (3.3%) and partial dehiscence in 3 cases (5.0%). At 6 months, 91.7% of patients reported satisfaction with cosmetic outcomes. The satisfied group demonstrated higher mean Visual Analog Scale scores (8.65 ± 1.03 vs 7.20 ± 0.45, P = .004) and lower mean VSS scores (1.74 ± 0.61 vs 2.90 ± 0.22, P < .001). Multivariate regression identified 2 independent predictors of satisfaction: lower VSS scores (odds ratio = 0.12, 95% confidence interval: 0.02-0.68, P = .016) and adherence to silicone therapy (odds ratio = 5.42, 95% confidence interval: 1.10-26.81, P = .038). In this non-comparative cohort, cosmetic suturing with layered closure and tension-reducing techniques, supplemented by scar management strategies, produced favorable short-term aesthetic outcomes in a rural Chinese hospital. However, interpretation is limited by the small sample size, 6-month follow-up period, and potential selection bias. Broader multicenter studies with longer follow-up are warranted to validate these findings.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".