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Dor no doente com câncer: características e controle

2022· article· pt· W3216298693 on OpenAlexaboutno aff
Cibele Andrucioli de Mattos Pimenta, María Sumie Koizumi, Manoel Jacobsen Teixeira

Bibliographic record

VenueRevista Brasileira de Cancerologia · 2022
Typearticle
Languagept
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecologyHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Este estudo objetivou compor o perfil da dor e do seu controle, além de verificar a influência de fatores terapêuticos na expressão do sintoma álgico em 57 doentes com doença neoplásica avançada, seqüencialmente atendidos no ambulatório de oncologia de um hospital geral. A dor foi moderada na maioria dos doentes e intensa em cerca de 1/5 dos casos, com duração média de 10 meses. Em 40,9 % das escolhas, observou-se preferência por 12 descritores do questionário de dor McGill. Descritores afetivos foram, significantemente, os mais escolhidos (p < 0,05). O alívio obtido foi insatisfatório, na maioria dos casos. O índice de controle da dor foi negativo em 49,1% dos doentes, isto é, em cerca de metade dos casos foram empregados analgésicos com potência inferior à exigida pela intensidade da dor. Não se observou correlação entre a intensidade da dor e a compatibilidade ou não dos esquemas analgésicos propostos ao padrão da OMS Constatou se que os doentes que fizeram uso dos analgésicos de modo regular, experienciaram dor de menor intensidade do que aqueles que só os utilizaram quando a dor se acentuava (p <0,05). Observou-se que a irregularidade na utilização dos fármacos associou-se a dores mais intensas.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.019
GPT teacher head0.283
Teacher spread0.263 · 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 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

Citations18
Published2022
Admission routes1
Has abstractyes

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