Efficacy of topical cadexomer iodine treatment in chronic wounds: Systematic review and meta‐analysis of comparative clinical trials
Bibliographic record
Abstract
Abstract The aim of this study was to summarise the clinical evidence supporting almost 40 years of topical cadexomer iodine (CIOD) use in wound bed preparation by removing barriers to healing such as exudate, slough, bioburden, and infection and allowing chronic wound progression. A systematic review was conducted (Embase/PubMed, November 2020) to identify relevant comparative studies meeting inclusion criteria. Meta‐analyses were performed using a fixed‐effects (I2 < 50%) or random‐effects model (I2 ≥ 50%) depending on statistical heterogeneity. Dichotomous outcomes were reported as relative risk (RR) and continuous outcomes as mean difference (MD), with 95% confidence intervals. In total, 436 publications were identified of which 13 were comparative trials including outcomes of interest. Significant reductions in exudate, pus/debris, slough, bioburden, and infection were reported in chronic wounds treated with CIOD, compared with standard of care (SOC). Meta‐analyses highlighted the positive impact of CIOD on mean wound area reduction (MD = 2.35 cm2, 95% CI = 0.34–4.36, P = .0219) after eight weeks treatment and overall wound healing events compared to SOC; wounds including venous leg ulcers, diabetic foot ulcers, and pressure ulcers treated with CIOD were more than twice as likely to heal than those receiving SOC (RR = 2.30, 95% CI = 1.54–3.45, P < .0001). This meta‐analysis demonstrates the efficacy of CIOD on chronic wounds through removal of barriers to healing. CIOD should be considered in wound bed preparation and treatment protocols.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".