Foam dressings: a review of the literature and evaluation of fluid-handling capacity of four leading foam dressings
Bibliographic record
Abstract
Posnett and Franks (2008) have calculated that 200,000 people in the UK have a chronic wound, with an estimated treatment cost of between £2.3 billion and £3.1 billion per year. With an ever-increasing ageing population, it can be assumed that costs associated with the management and treatment of wounds will also continue to rise. The Business Service Authority (2014) reported that in 2013 between £160 and £185 million was spent on wound care dressings within primary care services in England, of which foam dressings accounted for £22.6 million of the overall spend. Foam dressings are frequently used in wound care to assist with the management of wound exudate, helping to prevent maceration of the wound bed, protect the surrounding skin and prevent cross-infection caused by strikethrough. The aim of dressings is to provide an optimum environment at the interface with the wound bed to promote wound healing. With limited financial resources within health care, the cost-effectiveness of each type of wound dressing is high on the agenda. It is, however, important that costs are not considered in isolation; the outcomes (general health benefits) associated with interventions (e.g. wound healing and reduction in wound pain) must also be taken into account alongside close collaboration with the patient, and in some cases the carer (Rippon et al, 2008). This article provides a summary of the published literature relating to foam dressings, investigating their impact on healing rates, pain on dressing removal, fluid-handling capacity and their cost-effectiveness. It focuses on the independent assessment of the fluid-handling capacity of eight commonly-prescribed foam dressings: four bordered (Cutimed® Siltec B, Mepilex® Border, Allevyn® Life and Tegaderm™ foam adhesive) and four non-bordered (Cutimed® Siltec/Cutimed® SiltecPLUS, Mepilex®, Allevyn® Non-Adhesive, and Tegaderm™ foam).
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".