Seeking effective interventions to treat complex wounds: an overview of systematic reviews
Bibliographic record
Abstract
BACKGROUND: Numerous, often multi-faceted regimens are available for treating complex wounds, yet the evidence of these interventions is recondite across the literature. We aimed to identify effective interventions to treat complex wounds through an overview of systematic reviews. METHODS: MEDLINE (OVID interface, 1946 until October 26, 2012), EMBASE (OVID interface, 1947 until October 26, 2012), and the Cochrane Database of Systematic Reviews (Issue 10 of 12, 2012) were searched on October 26, 2012. Systematic reviews that examined adults receiving care for their complex wounds were included. Two reviewers independently screened the literature, abstracted data, and assessed study quality using the Assessment of Multiple Systematic Reviews (AMSTAR) tool. RESULTS: Overall, 99 systematic reviews were included after screening 6,200 titles and abstracts and 422 full-texts; 54 were systematic reviews with a meta-analysis (including data on over 54,000 patients) and 45 were systematic reviews without a meta-analysis. Overall, 44% of included reviews were rated as being of high quality (AMSTAR score ≥ 8). Based on data from systematic reviews including a meta-analysis with an AMSTAR score ≥ 8, promising interventions for complex wounds were identified. These included bandages or stockings (multi-layer, high compression) and wound cleansing for venous leg ulcers; four-layer bandages for mixed arterial/venous leg ulcers; biologics, ultrasound, and hydrogel dressings for diabetic leg/foot ulcers; hydrocolloid dressings, electrotherapy, air-fluidized beds, and alternate foam mattresses for pressure ulcers; and silver dressings and ultrasound for unspecified mixed complex wounds. For surgical wound infections, topical negative pressure and vacuum-assisted closure were promising interventions, but this was based on evidence from moderate to low quality systematic reviews. CONCLUSIONS: Numerous interventions can be utilized for patients with varying types of complex wounds, yet few treatments were consistently effective across all outcomes throughout the literature. Clinicians and patients can use our results to tailor effective treatment according to type of complex wound. Network meta-analysis will be of benefit to decision-makers, as it will permit multiple treatment comparisons and ranking of the effectiveness of all interventions. Please see related article: http://dx.doi.org/10.1186/s12916-015-0326-3.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".