The effect of beta-glucan on wound healing: a systematic review and meta-analysis
Bibliographic record
Abstract
Background wound healing involves inflammation, proliferation and remodeling, with prolonged healing times posing significant challenges.Beta-glucans, natural polysaccharides, may enhance this process.Objective To evaluate the impact of beta-glucan on wound healing.Method A systematic review of MEDLINE, Embase, Scopus, and Cochrane Central databases was conducted.Eligible studies included randomised controlled trials, clinical trials, cohort, and case-control studies comparing beta-glucan to other treatments.Only English-language studies were included, with no time restrictions.Screening and assessment were independently performed by two reviewers using Rayyan.The risk of bias was evaluated using the ROB-2 tool and the Newcastle-Ottawa Scale (NOS).A meta-analysis was performed on the included studies.Result Beta-glucans promote immune cell activation and tissue repair, accelerating inflammation resolution.The metaanalysis of chronic wounds included two studies comprising a total of 354 participants, demonstrating a twofold increase in chronic wound healing rates at 12 weeks with the application of topical beta-glucan.In contrast, the analysis of acute wounds included two studies with a combined sample size of 290 participants.However, the findings for acute wounds were inconclusive.Conclusion Beta-glucan demonstrates potential as an adjunctive therapy for chronic wounds, significantly accelerating healing rates.Its clinical utility warrants further exploration.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".