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Record W3110913921 · doi:10.21727/rpu.v11i2.2306

Encadeamentos da Doença Renal Crônica e o impacto na qualidade de vida de pacientes em hemodiálise

2020· article· pt· W3110913921 on OpenAlexaff
Wanderson Alves Ribeiro, Denilson da Silva Evangelista, Júlio César Figueiredo Júnior, Júlio Gabriel Mendonça de Sousa

Bibliographic record

VenueRevista Pró-UniverSUS · 2020
Typearticle
Languagept
FieldComputer Science
TopicHealthcare during COVID-19 Pandemic
Canadian institutionsImpact
Fundersnot available
KeywordsMedicineHemodialysisInternal medicine

Abstract

fetched live from OpenAlex

A doença renal crônica (DRC) trata-se de enfermidade caracterizada pela perda permanente e irreversível das funções dos rins, comumente associada a diabetes mellitus e hipertensão arterial e que pode progredir para uma fase mais severa, chamada de doença renal crônica terminal (DRCT). Trata-se de uma pesquisa bibliográfica de abordagem qualitativa e caráter descritivo. Como metodologia, utilizou-se a Biblioteca Virtual de Saúde, nas bases de informações LILACS, BDENF, MEDLINE e SCIELO, com recorte temporal de 2011 à 2018. O estudo objetivou em descrever os encadeamentos da Doença Renal Crônica na qualidade de vida de pacientes em hemodiálise e caracterizar o impacto na qualidade de vida de pacientes em hemodiálise. Após a leitura reflexiva dos artigos encontrados, emergiram 4 categorias: Doença Renal Crônica; As complexidades da Hemodiálise (HD) e da Fístula arteriovenosa (FAV); O impacto na Qualidade de vida dos pacientes com Doença Renal Crônica; O enfermeiro e sua atuação frente ao quadro clínico do paciente em uso de hemodiálise. Por fim, conclui-se que a complexidade no atendimento a pacientes com DRC se dá, em especial, pela abordagem do tratamento que será adotada. Isso se dá pelo fato de que ao se diagnosticar a Doença renal crônica (DRC), será preciso avaliar também a viabilidade dos tratamentos disponíveis, existência de outras doenças, além dos fatores sociais e emocionais que possam ter influência sobre a terapêutica.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0010.002
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 head0.067
GPT teacher head0.335
Teacher spread0.268 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRevista Pró-UniverSUSSame topicHealthcare during COVID-19 PandemicFrench-language works237,207