Local Insulin Improves Wound Healing: A Systematic Review and Bayesian Network Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Wounds are a significant health issue, and reliable and safe strategies to promote repair are needed. Clinical trials have demonstrated that local insulin promotes healing in acute and chronic wounds (ie, reductions of 7% to 40% versus placebo). However, the trials' sample sizes have prevented drawing solid conclusions. Furthermore, no analysis has focused on safety concerns (ie, hypoglycemia). Under the hypothesis that local insulin promotes healing through proangiogenic effects and cellular recruitment, the aim of this systematic review and network meta-analysis (NMA) was to assess its safety and relative effectiveness using a Bayesian approach. METHODS: Medline, CENTRAL, Embase, Scopus, LILACS, and gray literature sources were searched for human studies assessing the local use of insulin versus any comparator since inception to October of 2020. Data on glucose changes and adverse events, wound and treatment characteristics, and healing outcomes were extracted, and an NMA was conducted. RESULTS: A total of 949 reports were found, of which 23 ( n = 1240 patients) were included in the NMA. The studies evaluated six different therapies, and most comparisons were against placebo. NMA showed -1.8 mg/dL blood glucose level change with insulin and a lack of reported adverse events. Statistically significant clinical outcomes identified include reduction in wound size (-27%), increased healing rate (23 mm/day), reduction in Pressure Ulcer Scale for Healing scores (-2.7), -10 days to attain complete closure, and an odds ratio of 20 for complete wound closure with insulin use. Likewise, significantly increased neoangiogenesis (+30 vessels/mm 2 ) and granulation tissue (+25%) were also found. CONCLUSION: Local insulin promotes wound healing without significant adverse events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".