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Aplicação da versão brasileira do questionário de dor Mcgill em idosos com dor crônica

2006· article· pt· W4296926833 on OpenAlexaboutno aff
Clarissa Cardoso dos Santos, Leani Souza Máximo Pereira, Marcos Antônio de Resende, Frederico Magno, Vanessa Aguiar

Bibliographic record

VenueActa Fisiátrica · 2006
Typearticle
Languagept
FieldComputer Science
TopicHealthcare during COVID-19 Pandemic
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPsychologyElderly peopleGerontologyMedicinePhilosophy

Abstract

fetched live from OpenAlex

A dor crônica é uma experiência multidimensional que envolve aspectos sensório-perceptual, afetivo-motivacional e cognitivo-avaliativo que se interagem e contribuem para a resposta dolorosa final. As alterações características do processo do envelhecimento em cada um desses aspectos podem interferir na experiência da dor, dificultando a sua avaliação adequada. O uso de uma escala multidimensional como o Questionário de dor McGill (MPQ) possibilita uma avaliação mais adequada desse sintoma. Os objetivos desse estudo foram verificar a confiabilidade intra e inter examinadores da aplicação do Br-MPQ em idosos com dor crônica em decorrência de doenças ortopédicas e neurológicas.Participaram desse estudo 19 idosos com doenças ortopédicas (71,21 ± 7,51 anos) e 19 idosos com doenças neurológicas (69,79 ± 5,30 anos) apresentando o diagnóstico de dor crônica, encaminhados pelo serviço médico, sem alterações cognitivas. A confiabilidade geral intra e interexaminadores nos idosos com doenças ortopédicas foram 0,86 e 0,89, respectivamente, e para idosos com doenças neurológicas de 0,71 e 0,68, respectivamente (Spearman, p<0,05). Os resultados mostraram que o Br-MPQ foi de fácil aplicação (8,54 ± 2,35 minutos) nessa amostra. O presente estudo demonstrou que o Br-MPQ pode ser mais adequado para avaliar a dor crônica em idosos, uma vez que a percepção desse sintoma está mais relacionada aos aspectos sensoriais, afetivos e cognitivo-avaliativos e não somente à intensidade.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0040.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.034
GPT teacher head0.309
Teacher spread0.275 · 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

Citations15
Published2006
Admission routes1
Has abstractyes

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