Efficacy of Cosmetic Debridement and Suture With Recombinant Human <scp>EGF</scp> in Maxillofacial Trauma: A Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: To analyze and evaluate the clinical effect of cosmetic debridement and suture combined with recombinant human epidermal growth factor (rhEGF). METHODS: A systematic review of the literature was performed by searching China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Chinese Science and Technology Journals, China Biomedicine, PubMed, Web of Science, and Cochrane Library. RevMan 5.4.1 software was used for statistical analysis. Heterogeneity among studies was assessed using the Q test (p value). Publication bias was evaluated via funnel plots, forest plots were generated, and the combined odds ratio (OR) was calculated using a fixed-effects model or random-effects model. RESULTS: The combined therapy showed favorable clinical efficacy [OR = 6.62, 95% confidence interval (95% CI) (3.14-13.92), p < 0.00001], shorter wound healing time [mean difference (MD) = -2.69, 95% CI (-3.10 to -2.29), p < 0.00001], and improved scar outcomes (lower Vancouver Scar Scale (VSS) and Patient and Observer Scar Assessment Scale (POSAS) scores) at 6 months. Serum epidermal growth factor (EGF) levels were higher, while interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) levels were lower in the combined therapy group (all p < 0.05). CONCLUSION: Aesthetic debridement and suture combined with rhEGF have a good clinical effect in the treatment of maxillofacial trauma.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".