Clinical Study of Rh-bFGF Combined With Collagen Sponge in the Treatment of Maxillofacial Deep Ⅱ Degree Burn
Bibliographic record
Abstract
OBJECTIVES: To observe the clinical effect of recombinant human alkaline fibroblast growth factor (rh-bFGF) combined with collagen sponge in the treatment of maxillofacial deepⅡ degree burn. METHODS: From January 2019 to January 2022, 96 patients with maxillofacial deep Ⅱ degree burns were randomly divided into a control group (N=48) and an observation group (N=48). The observation group was treated with rh-bFGF and collagen sponge after debridement, whereas the control group was treated with silver sulfadiazine ointment after debridement. The healing rate and healing time of the wounds were observed, interleukin (IL)-6, tumor necrosis factor (TNF)-α, IL-10, epidermal growth factor (EGF), endothelial growth factor growth factor (VEGF), and metalloproteinase tissue inhibitor 1 (TIMP-1) were measured. Vancouver Scar Scale (VSS) was used to evaluate the local scar at 6 months after wound healing in both groups. RESULTS: On the 10th, 14th, and 21st day of treatment, the wound healing rate in the observation group was higher than that in the control group (P<0.05), the wound healing time in the observation group was lower than that in the control group (P<0.05), and on the 14th day of treatment, the levels of TNF-α and IL-6 in the observation group were lower than those in the control group (P<0.05). The levels of IL-10 in the observation group were higher than those in the control group (P<0.05). The levels of EGF, VEGF, and TIMP-1 in the observation group were higher than those in the control group (P<0.05), and the scores of VSS in the observation group were lower than those in the control group (P<0.05). CONCLUSIONS: Rh-bFGF combined with collagen sponge can decrease the levels of TNF-α and IL-6 and increase the levels of IL-10, which can control the inflammation effectively, at the same time, it can increase the level of EGF, VEGF, and TIMP-1, promote wound healing, and reduce scar hyperplasia. The treatment protocol is simple, safe, effective, and suitable for clinical application.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".