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Record W2132351228 · doi:10.1177/1084822313495734

Pressure Ulcer Risk Assessment

2013· article· en· W2132351228 on OpenAlexaff
Ronald Kelly, Gloria Puurveen

Bibliographic record

VenueHome Health Care Management & Practice · 2013
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsUniversity of British ColumbiaFraser Health
Fundersnot available
KeywordsProxy (statistics)MedicineRisk assessmentScale (ratio)Nursing assessmentReceiver operating characteristicMEDLINEInternal medicineStatisticsComputer science

Abstract

fetched live from OpenAlex

This article is a report on a study to develop a pressure ulcer risk assessment scale for home care clients. Multiple linear regressions were used to model scores on the Braden assessment scale and subcales, using data from the Resident Assessment Instrument—Home Care (RAI-HC) assessment. In Phase 1, data from 510 home care clients who received both assessments within a 14-day period were used to develop the models. Suitable “proxy Braden” models were constructed for the Braden scale and all subscales except for the nutrition subscale. In Phase 2, receiver operating curves revealed the proxy Braden to be a significantly better predictor than the RAI-HC Pressure Ulcer Risk Scale (PURS) of pressure ulcer development in home care clients.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.429
Teacher spread0.410 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations9
Published2013
Admission routes1
Has abstractyes

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