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A influência de variáveis sociodemográficas, clínicas e funcionais sobre a qualidade de vida de idosos com artroplastia total do quadril

2010· article· pt· W2050073812 on OpenAlexaboutno aff
Mariana Kátia Rampazo, Maria José D’Elboux

Bibliographic record

VenueBrazilian Journal of Physical Therapy · 2010
Typearticle
Languagept
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

OBJETIVOS: Avaliar a qualidade de vida relacionada à saúde (QVRS) de idosos com Artroplastia Total de Quadril (ATQ) e investigar a relação e a influência de variáveis sociodemográficas, clínicas e funcionais nesses sujeitos. MÉTODOS: A QVRS foi avaliada por meio das versões brasileiras dos instrumentos genérico The Medical Outcomes Study 36-item Short-Form Health Survey (SF-36) e específico Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) em 88 idosos com ATQ primária e unilateral de ambos os gêneros. Os dados foram submetidos às análises estatísiticas: descritiva; análise de variância univariada (ANOVA) e multivariada (MANOVA) para verificar a influência das variáveis estudadas nas dimensões do SF-36 e do WOMAC e testes de Mann-Whitney e Kruskal-Wallis para comparação dos escores dos instrumentos entre as variáveis. RESULTADOS: A amostra estudada teve predomínio das mulheres, e a média de idade foi de 68,8(±7,4) anos. A função do quadril, avaliada pelo Harris Hip Score, foi a variável que apresentou influência significativa na QVRS sob a perspectiva do instrumento genérico e do específico. O uso de acessórios para a locomoção, a função do quadril e a satisfação com a cirurgia foram as principais variáveis que apresentaram diferenças significativas nas dimensões do SF-36 e do WOMAC. CONCLUSÕES: Investimentos no âmbito funcional e programas de reabilitação direcionados às peculiaridades dos idosos com ATQ podem beneficiar essa população.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.307
Teacher spread0.295 · 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 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

Citations2
Published2010
Admission routes1
Has abstractyes

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