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Lesão por pressão relacionada a dispositivos médicos: prevenção e fatores de risco associados

2021· article· pt· W3215605559 on OpenAlexaff
Carla Nascimento Souza Santos, Gabriela Maia de Oliveira, Flávia Carla Takaki Cavichioli, Hélio Martins do Nascimento Filho, Fabíola Arantes Ferreira, Loaniela Tinti Moreira Borges

Bibliographic record

VenueNursing Edição Brasileira · 2021
Typearticle
Languagept
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsNovelis (Canada)
Fundersnot available
KeywordsHumanitiesSciELOMedicinePhilosophyMEDLINEPolitical science

Abstract

fetched live from OpenAlex

Introdução: A lesão por pressão é encontrada na pele ou tecido subjacente, geralmente sobre uma proeminência óssea, resultante da exposição à pressão ou forças de cisalhamento, possuindo fatores intrínsecos e extrínsecos: imobilidade, inconsciência, perda da continência urinária e fecal, deficiência nutricional, doenças crônico degenerativas, peso e relacionados a dispositivos médicos. Objetivo: Descrever a prevenção e fatores de risco para Lesão por pressão relacionadas à dispositivos médicos. Método: Revisão integrativa de literatura, entre 201 O a 2020 nas bases de dados LILACS, PubMed, Bdenf e site de busca Scielo. Resultados: Foram incluídos nove estudos, sendo: quatro (44,4%) publicações que descrevem os principais dispositivos relacionados a estas lesões. Conclusão: Foi descrito os fatores associados ao desenvolvimento de Lesões por Pressão Relacionadas a Dispositivos Médicos e como preveni-las, identificando quais os dispositivos de risco, e medidas de prevenção e tratamento, cuidados específicos e eficazes por meio dos profissionais de enfermagem na prevenção e tratamento.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.600
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.052
GPT teacher head0.405
Teacher spread0.354 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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