Wound Growth Factors Meta-Analysis – Do They Work?
Bibliographic record
Abstract
PURPOSE: Topical Growth Factors are important future pillars in wound healing but have limited evidence based literature. Meta-analysis and systematic review on all growth factors was performed. METHODS: All human randomised control trials (RCTs) involving wound and growth factor on Pubmed, Ovid, and Cochrane register and non-randomised comparative retrospective analyses with large patient numbers were included for their statistical weight (n=51). Forest plots were done. RESULTS: Five main groups were identified: Platelet derived Growth factor (PDGF), platelet releasate/gel (PR), Fibroblast Growth factor (FGF), Epidermal Growth Factor (EGF) and Granulocyte Colony Stimulating Factor (GCSF). The combined RCTs patient number in each group was: PDGF (n=3520), PR (n=6441), FGF (n=1683), EGF (n=517) and GCSF (n=452). Wounds (diabetic, neuropathic pressure, hypertensive and venous ulcers, burns and skin graft) and trial length for healing to occur were heterogenous. The relative risk of complete healing compared to placebo is 2.26-4.7 (EGF), 1.41-7.68 (PDGF), 1.33-1.68 (FGF), 1.37-3.21 (GCSF) and 1.32 (PR). The ratio of mean/median days to complete healing compared to placebo is 0.68 (PDGF), 0.60 (EGF), 0.58-0.91 (FGF), 0.55-0.72 (GCSF). The ratio of mean/median percentage area of wound healed compared to placebo is 1.2-3.5 (PDGF), 3.4 (PR), 1.04-1.20 (FGF), 1.49-1.93 (GCSF). All ratios were statistically significant (p<0.05). EGF and FGF had better Vancouver scar scale ratings compared to placebo. No significant differences in adverse events were noted. DISCUSSION/CONCLUSION: Clinical use of topical PDGF, PR, FGF, EGF and GCSF have statistically significant faster rates of healing (especially higher in first 2 weeks), higher chances of complete healing and possible better scar profiles. Cochrane reviews, economic cost analyses, larger trials that overcome insufficiently powered RCTs, standardized outcome definitions and trial length are needed for further widespread clinical adoption. New growth factors (e.g: Vascular Endothelial Growth Factor (VEGF) and Keratinocyte Growth Factor (KGF)) are also being tested
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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 | 0.074 | 0.167 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.067 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".