Developments in silicone technology for use in stoma care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Soft silicone's flexibility, adhesive capacity and non-toxic, non-odourous and hypoallergenic nature have made it an established material for adhesive and protective therapeutic devices. In wound care, silicone is a component of contact layer dressings for superficial wounds and silicone gel sheeting for reducing the risk of scarring, as well as of barriers for incontinence-associated dermatitis. Regarding stoma accessories, silicone is established in barrier films to prevent contact dermatitis, adhesive removers to prevent skin stripping and filler gels to prevent appliance leaks. Until recently, silicone has not been used in stoma appliances flanges, as its hydrophobic nature has not allowed for moisture management to permit trans-epidermal water loss and prevent maceration. Traditional hydrocolloid appliances manage moisture by absorbing water, but this can lead to saturation and moisture-associated skin damage (MASD), as well as increased adhesion and resultant skin tears on removal, known as medical adhesive-related skin injury (MARSI). However, novel silicone compounds have been developed with a distinct evaporation-based mechanism of moisture management. This uses colloidal separation to allow the passage of water vapour at a rate equivalent to normal trans-epidermal water loss. It has been shown to minimise MASD, increase wear time and permit atraumatic removal without the use of adhesive solvents. Trio Healthcare has introduced this technology with a range of silicone-based flange extenders and is working with the University of Bradford Centre for Skin Sciences on prototype silicone-based stoma appliance flanges designed to significantly reduce the incidence of peristomal skin complications, such as MARSI and MASD. It is hoped that this will also increase appliance wear time, reduce costs and improve patient quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it