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Record W4389372950 · doi:10.56083/rcv3n11-194

IMPACTO DO BAD RAGAZ NA QUALIDADE DE VIDA E DOR EM PACIENTES COM LOMBALGIA CRÔNICA

2023· article· pt· W4389372950 on OpenAlexaboutno aff
Yana Bernarde Sá, Thiago Pereira Lemos, Alex Ripardo Da Silva, Danilo Reis Barbosa, Daphne Teodosio de Arruda, Miriam Eloana Lopes Bacelar, Rodrigo Luí­s Ferreira Da Silva, Mariana dos Anjos Furtado De Sá

Bibliographic record

VenueRevista Contemporânea · 2023
Typearticle
Languagept
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)McGill Pain QuestionnairePhysical therapyGynecologyPsychologyGerontologyVisual analogue scaleNursing

Abstract

fetched live from OpenAlex

Objetivo: Analisar o impacto do Bad Ragaz na qualidade de vida e dor em pacientes adultos com lombalgia crônica. Métodos: Trata-se de um estudo de corte transversal, quantitativo e comparativo desenvolvido através de um protocolo com exercícios de Bad Ragaz e Hidrocinesioterapia no Ambulatório de Hidroterapia da Universidade do Estado o Pará, utilizando instrumentos de quantificação como Escala de Autoefícácia para Dor Crônica ou Chronic Pain Self-efficacy Scale (CPSS), Escala Visual Analógica (EVA) e Questionário WHOQOL-bref. Resultados: A amostra foi composta por N= 14 participantes divididos igualmente em 2 grupos (7 indivíduos em cada grupo). Foi demonstrado que houve melhora significativa na dor no grupo Bad Ragaz nos questionários EVA (p < 0,05) e CPSS (p < 0,05), em relação a qualidade de vida, avaliada pelo Questionário WHOQOL-Bref, não houve melhora significativa no grupo Bad Ragaz, apenas no domínio físico no grupo Hidrocinesioterapia. Conclusão: As técnicas aplicadas em ambos os grupos de estudo mostraram-se igualmente benéficas na dor lombar crônica, porém em relação a qualidade de vida não houve impacto significativo.

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)0.005
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.024
GPT teacher head0.327
Teacher spread0.303 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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