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Record W3137113514 · doi:10.25248/reas.e6573.2021

Avaliação da dor em pacientes com diagnóstico de câncer de colo do útero em Sergipe

2021· article· pt· W3137113514 on OpenAlexaboutno aff
Delza Correia Lima, Maria Nathália Prado Simões Mendonça, Ayla Gabriella Silva Ribeiro, Luísa Teixeira Silveira, Tiago Almeida Costa, Marina de Pádua Nogueira, Bruna Nogueira Viana

Bibliographic record

VenueRevista Eletrônica Acervo Saúde · 2021
Typearticle
Languagept
FieldMedicine
TopicWomen's cancer prevention and management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecologyPhysics

Abstract

fetched live from OpenAlex

Objetivo: Avaliação da dor em pacientes com câncer de colo uterino atendidos no Hospital de Urgência de Sergipe (HUSE), tanto do ponto de vista qualitativo quanto quantitativo. Métodos: Estudo quantitativo, descritivo, tipo exploratório, de corte transversal, realizado no ambulatório de Oncologia do HUSE, no município de Aracaju, entre 2010 e 2014. A pesquisa foi realizada em 53 pacientes diagnosticadas e tratadas com câncer de colo de útero e a coleta de dados foi realizada por meio de questionários, como o de Mcgill e a Escala Visual Analógica. Resultados: A queixa álgica foi um fenômeno relatado por 34 pacientes (64,1%), seja no diagnóstico, no tratamento, na reabilitação ou em mais de uma dessas etapas. Quanto à intensidade da dor, 22 entrevistadas (41,5%) apresentaram dor intensa e 19 (35,8%) não apresentaram dor. Ao analisar os descritores da dor, observou-se que dentre as 34 pacientes que referiam a queixa álgica, a categoria sensorial foi preponderante, seguida pela afetiva. O descritor sensitivo mais utilizado foi dor fina com 10,6%, já no caráter afetivo foi dor enjoada com 22,9%. Conclusão: A dor intensa esteve presente na maioria das pacientes em algum estágio da doença e o caráter sensorial da queixa álgica foi predominante no estudo.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.021
GPT teacher head0.306
Teacher spread0.285 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

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