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Record W1534592189 · doi:10.3390/healthcare3030574

Improving Outcomes by Implementing a Pressure Ulcer Prevention Program (PUPP): Going beyond the Basics

2015· article· en· W1534592189 on OpenAlexaboutno aff
Amparo Cano, Debbie Anglade, Hope Stamp, Fortunata Joaquin, Jennifer López, Lori Lupe, Steven Schmidt, Daniel L. Young

Bibliographic record

VenueHealthcare · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionQuarter (Canadian coin)MedicineMiamiHealth careIncidence (geometry)GerontologyNursingFamily medicinePolitical science

Abstract

fetched live from OpenAlex

A multidisciplinary process improvement program was initiated at the University of Miami Hospital (UMH) in 2009 to identify the prevalence of hospital-acquired pressure ulcers (HAPU) at the institution and to implement interventions to reduce the incidence of HAPU. This deliberate and thoughtful committee-driven process evaluated care, monitored results, and designed evidence-based strategic initiatives to manage and reduce the rate of HAPU. As a result all inpatient beds were replaced with support surfaces, updated care delivery protocols were created, and monitored, turning schedules were addressed, and a wound, ostomy, and continence (WOC) nurse and support staff were hired. These initial interventions resulted in a decrease in the prevalence of HAPU at UMH from 11.7% of stage II to IV ulcers in the second quarter, 2009 to 2.1% the third quarter. The rate remained at or near the 2009 UMH benchmark of 3.1% until the first quarter of 2012 when the rate rose to 4.1%. At that time new skin products were introduced into practice and continuing re-education was provided. The rate of HAPU dropped to 2.76% by the second quarter of 2012 and has remained steadily low at 1%-2% for nine consecutive quarters.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.085
GPT teacher head0.461
Teacher spread0.375 · 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.

Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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