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Record W3002761553 · doi:10.33233/eb.v18i6.2893

Características sociodemográficas e clínicas de indivíduos com insuficiência cardíaca associadas í  classe funcional da doença

2020· article· pt· W3002761553 on OpenAlexaff
Fernanda Rafaela de Carvalho Martins, Glícia Gleide Gonçalves Gama, Andréia Santos Mendes

Bibliographic record

VenueEnfermagem Brasil · 2020
Typearticle
Languagept
FieldSocial Sciences
TopicAcademic Research in Diverse Fields
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsMedicineHumanitiesArt

Abstract

fetched live from OpenAlex

Introdução: A insuficiência cardí­aca (IC) é uma sí­ndrome que se apresenta com crescente prevalência, podendo limitar o indiví­duo quanto í capacidade fí­sico-funcional, e condição pulmonar. Objetivo: Descrever as caracterí­sticas sociodemográficas e clí­nicas de indiví­duos com IC assistidos em unidade cardiovascular segundo classe funcional da New York Heart Association (NYHA). Métodos: Estudo retrospectivo, quantitativo, realizado a partir de dados coletados em 120 prontuários de indiví­duos com IC que estiveram hospitalizados na unidade cardiovascular de um Hospital Universitário (HU), no municí­pio de Salvador/BA. Utilizou-se instrumento próprio que incluiu dados sociodemográficos e clí­nicos da IC. Resultados: Dos 120 sujeitos estudados, 53% tinham mais de 60 anos, 28,5% correspondendo a classe funcional (CF) IV. A distribuição foi equitativa entre os sexos, com uma pequena prevalência de mulheres na CF III (36,7%). Os pacientes que possuí­am fração de ejeção <50% estavam entre as CF III (33,8%) e CF IV (31,0%). Além disso, 41,6% dos pacientes permaneceram no hospital entre 8 e 30 dias, e 87,5% evoluiu com alta hospitalar. Conclusão: Indiví­duos idosos e autodeclarados negros com IC apresentam evolução clí­nica mais grave relacionada ao aumento da classe funcional da NYHA.Palavras-chave: insuficiência cardí­aca, adulto, Enfermagem.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.204
GPT teacher head0.409
Teacher spread0.205 · 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 designObservational
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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