MétaCan
Menu
Back to cohort

Avaliação das dimensões da dor no paciente oncológico

2020· article· pt· W3080669861 on OpenAlexfundaboutno aff
Dara Brunner Borchartt, Kelly Cristina Meller Sangoi, Rosane Teresinha Fontana, Jane Conceição Perin Lucca, Márcia Betana Cargnin

Bibliographic record

VenueNursing Edição Brasileira · 2020
Typearticle
Languagept
FieldMedicine
TopicPalliative and Oncologic Care
Canadian institutionsnot available
FundersMcGill University
KeywordsHumanitiesMedicinePhilosophy

Abstract

fetched live from OpenAlex

Objetivo: mensurar a experiência dolorosa em pacientes oncológicos. Método: pesquisa transversal, descritiva de abordagem quantitativa, com 50 pacientes que realizam tratamento oncológico em um serviço privado localizado na região Noroeste do Estado do Rio Grande do Sul, após aprovação do Comitê de Ética em Pesquisa sob protocolo CAAE nº 14025119.0.0000.5354. O instrumento escolhido foi o Questionário da Dor McGill. Os dados foram armazenados no Microsoft Office Excel e analisados através da estatí­stica descritiva. Resultados: Prevalência do sexo feminino (54%) e câncer de Cólon (20%). Os descritores mais usados foram: Fisgada (54%), Cansativa (52%), Chata (38%) e Aperta (26%) e 80% dos pacientes relataram ausência de dor no momento da entrevista. Conclusão: O Questionário da Dor McGill permite conhecer os aspectos qualitativos da dor, além de dar suporte í enfermagem no planejamento da assistência ao paciente, oferecendo melhora na qualidade da sistematização da assistência de 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)0.004
metaresearch head score (Gemma)0.019
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.095
GPT teacher head0.381
Teacher spread0.286 · 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".

Quick stats

Citations3
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueNursing Edição BrasileiraSame topicPalliative and Oncologic CareFrench-language works237,207