Clinical efficacy of rhGM-CSF gel and medical collagen sponge on deep second-degree burns of infants: A randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: This study aimed to observe clinical efficacy of recombinant human granulocyte-macrophage colony-stimulating factor (rhGM-CSF) gel, medical collagen sponge and rhGM-CSF gel in combination with medical collagen sponge on deep second-degree burns of head, face or neck in infants. METHODS: A total of 108 infants with deep second-degree burns on head, face or neck were randomly divided into rhGM-CSF group, medical collagen sponge group, and rhGM-CSF + medical collagen sponge group. The scab dissolving time, healing time, bacterial positive rate and Vancouver scar scale were evaluated and analyzed. RESULTS: The data analysis showed that scab dissolving time and healing time were shorter in rhGM-CSF + medical collagen sponge group than that in rhGM-CSF group and medical collagen sponge group, and the difference was statistically significant (P < .05). Bacterial positive rate was lower in rhGM-CSF + medical collagen sponge group than that in rhGM-CSF group and medical collagen sponge group (P < .05). After 3 months, score of Vancouver scar scale (scar thickness, pliability, pigmentation and vascularity) was less in rhGM-CSF + medical collagen sponge group than that in rhGM-CSF group and medical collagen sponge group (P < .05). CONCLUSION: rhGM-CSF gel in combination with medical collagen sponge is significantly effective in treating deep second-degree burns of head, face or neck in infants. This combination is beneficial for infection control, acceleration of scab dissolving and wound healing, and reduction of scar hyperplasia and pigmentation, which is worthy of clinical application and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".