Growth factors for treating chronic venous leg ulcers: A systematic review and meta‐analysis
Bibliographic record
Abstract
Chronic venous leg ulcers (VLU) are wounds that commonly occur due to venous insufficiency. Many growth factors have been introduced over the past two decades to treat VLU. This systematic review and meta-analysis evaluates the impact of growth factor treatments of VLU in comparison to control for complete wound healing, percent reduction in wound area, time to wound healing, and adverse events. A systematic review and meta-analysis of randomised trials was conducted. MEDLINE and EMBASE were searched up to December 2020. Studies were included if they compared a growth factor versus placebo or standard care in patients with VLU. From 1645 articles, 13 trials were included (n = 991). There was a significant difference between any growth factor and placebo in complete wound healing (P = 0.04). Any growth factor compared to placebo significantly increased the likelihood of percent wound reduction by 48.80% (P = <0.00001). There was no difference in overall adverse event rate. Most comparisons have low certainty of evidence according to Grading of Recommendations, Assessment, Development, and Evaluation. This meta-analysis suggests that growth factors have a beneficial effect in complete wound healing of VLU. Growth factors may also increase percent reduction in wound area. The suggestion of benefit for growth factors identified in this review is not a strong one based on the low quality of evidence.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.019 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".