Recombinant human granulocyte macrophage colony stimulating factor in deep second-degree burn wound healing
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to explore the effects of recombinant human granulocyte macrophage colony stimulating factor (rhGM-CSF) on deep second-degree burn wound healing. METHODS: In this study, 95 patients with a total of 190 burn wounds were treated with either rhGM-CSF or placebo, separated into 2 groups by treatment type. Wound healing rate, wound healing time, histopathological condition, and scar scale were all compared between the 2 groups. RESULTS: The healing rates in the rhGM-CSF group were remarkably higher than those in the placebo group (P < .01). The wound healing time in the rhGM-CSF group (18.8 ± 7.6 days) was significantly shorter than that in the placebo group (25.5 ± 4.6 days, P < .01). On the 14th day and 28th day, the average optical density of vascular endothelial factor (VEGF) in the rhGM-CSF group was larger than that in the placebo group. Meanwhile, the average optical density of fibroblast growth factor (FGF) in the rhGM-CSF group was also larger than that in the placebo group. Furthermore, the Vancouver scar scale scores of pigmentation, pliability, height, and vascularity were notable lower in the rhGM-CSF group than those in the placebo group (P < .01). CONCLUSION: The results suggest that rhGM-CSF can significantly accelerate deep second-degree burn wound healing.
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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) | 0.000 | 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".