Impact of Textiles on Formation and Prevention of Skin Lesions and Bedsores
Bibliographic record
Abstract
A bedsore or pressure ulcer is an area of localized damage to the skin and underlying tissue caused by pressure, shear, friction, or a combination of these factors. In countries with a large geriatric population like Canada, this healthcare threat presents a significant risk to hospitalized patients, imposing huge cost on both treatment and care for patients. The role textiles play in the formation and prevention of pressure ulcers is understudied. The fact remains, textiles, such as clothing and bedding, have a considerable influence on factors, such as pressure, shear/friction, and skin hydration, which contribute to skin ulceration. Our work is a pilot study to investigate the role of textile products in the formation and prevention of bedsores. This study began with a survey study at a local long-term care facility, collecting information about incidences of bedsores and the physical conditions of residents. Information was also collected about the textile products that have been used by the residents. Correlations were established between these products and the incidence/severity of bedsores. Immobility of residents was determined to be a significant factor of causing skin lesions and pressure ulcers. Immobility of residents contributes to a prolonged interaction between skin and fabrics and might increase the chances of skin lesions or bedsores.
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.
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 | 0.001 | 0.003 |
| 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) | 0.004 | 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".