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Epidermólise bolhosa: foco na assistência de enfermagem

2016· article· pt· W2463356478 on OpenAlexaboutno aff
Cláudia Daniella Avelino Vasconcelos Benício, Nalma Alexandra Rocha de Carvalho, José Diego Marques Santos, Isabela Ribeiro de Sá Guimarães Nolêto, Maria Helena Barros Araújo Luz

Bibliographic record

VenueRevista Estima · 2016
Typearticle
Languagept
FieldHealth Professions
TopicNeonatal skin health care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

A epidermólise bolhosa é uma doença hereditária ocasionada por mutações em várias proteínas estruturais da pele, representando um quadro clínico grave, cujos defeitos adquiridos ou congênitos da adesão intraepidérmica ou dermoepidérmica levam à formação de bolhas (vesículas) na pele e nas mucosas, podendo ser espontâneas ou provocadas por trauma mínimo. Objetivou-se neste estudo identificar, por meio da literatura científica, temas importantes relacionados à assistência de enfermagem frente ao paciente com epidermólise bolhosa. Tratou-se de uma revisão integrativa de literatura com pesquisa realizada nas bases de dados MEDLINE (via PubMed) e PubMed Central Canada (PMC Canada). Oito artigos científicos compuseram a amostra deste estudo. Foi evidenciado um ambiente sofrível tanto para o paciente como para o enfermeiro durante o tratamento, bem como foram encontradas dificuldades pelo paciente em dispor de curativos e coberturas eficientes para as feridas. O impacto da epidermólise no ambiente psicossocial também foi destacado, com enfoque para o tempo gasto na troca de curativos. Concluiu-se que a enfermagem desempenha um papel relevante no tratamento do paciente com epidermólise bolhosa, haja vista as condições que tal sujeito enfrenta e o potencial do enfermeiro para efetuar cuidados paliativos

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)0.004
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: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.002
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.100
GPT teacher head0.444
Teacher spread0.344 · 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
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
Published2016
Admission routes1
Has abstractyes

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