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A New Model to Identify Shared Risk Factors for Pressure Ulcers and Frailty in Older Adults

2009· review· en· W2098354612 on OpenAlexaff
Karen E. Campbell

Bibliographic record

VenueRehabilitation Nursing · 2009
Typereview
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineIncidence (geometry)GerontologyAffect (linguistics)Relevance (law)Older peoplePsychology

Abstract

fetched live from OpenAlex

Pressure ulcers substantially affect frail older adults, as well as their family caregivers, nurses, and the healthcare system at large. This article uses the World Health Organization's International Classification of Functioning, Disability and Health as a framework to discuss pressure ulcers in frail older adults. Pressure ulcers and frailty are defined and prevalence and incidence figures are reviewed. The connection is made between pressure ulcers and frailty using a model of shared risk factors and these medical syndromes are described as a synergistic relationship. In addition, the relevance to practice is discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.005
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.086
GPT teacher head0.489
Teacher spread0.403 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations22
Published2009
Admission routes1
Has abstractyes

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