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Record W1999106950 · doi:10.1080/15569520701856765

Impact of Textiles on Formation and Prevention of Skin Lesions and Bedsores

2008· article· en· W1999106950 on OpenAlexaffabout
Wen Zhong, Ayyaz Ahmad, Malcolm Xing, Pat Yamada, Carole Hamel

Bibliographic record

VenueCutaneous and Ocular Toxicology · 2008
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsRiverview HospitalUniversity of Manitoba
Fundersnot available
KeywordsMedicineClothingPopulationDermatologySurgeryEnvironmental health

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.392
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2008
Admission routes2
Has abstractyes

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