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Record W3007897934 · doi:10.34024/rnc.2011.v19.8336

Utilização da CIF em pacientes com sequelas de AVC

2001· article· pt· W3007897934 on OpenAlexaboutno aff
Ana Irene Costa de Oliveira, Katyana Rocha Mendes da Silveira

Bibliographic record

VenueRevista Neurociências · 2001
Typearticle
Languagept
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecologyPsychologyPhysicsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Objetivo. O estudo consistiu em avaliar as funções e restrições de pacientes com sequela de AVC utilizando como ferramenta a CIF, incentivar seu uso e mostrar que pode permitir uma nova visão das condições de saúde. Método. Foram avaliados cinco pacientes de ambos os gêneros com quadro de hemiparesia, que se encontravam em atendimento fisioterapêutico na Clínica Escola de Fisioterapia da Universidade São Francisco (USF). Foram utilizadas ficha de avaliação neurológica, Escala de Qualidade de Vida Específica para AVE (EQVE-AVE) e a Canadian Occupational Performance Measure (COPM) para mensurar os aspectos referentes aos componentes da CIF. Resultados. Todos os pacientes apresentaram déficits de funcionalidade e restrições em suas Atividades de Vida Diária (AVD’s), todos de forma diferente devido aos fatores pessoais e ambientais, levando-os a um comprometimento psicossocial. Conclusão. A CIF é uma importante ferramenta que pode avaliar o paciente com AVC, pois observa o paciente de uma forma global (funcionalidade), sabendo que fatores pessoais e ambientais estão diretamente relacionados com a forma com que se apresenta na vida. Por ser uma ferramenta que tem visão ampla do paciente é importante para a prática clínica, pois oferece vários artifícios que melhoram a avaliação e auxilia na elaboração de um programa de tratamento individualizado.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.043
GPT teacher head0.303
Teacher spread0.259 · 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 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

Citations14
Published2001
Admission routes1
Has abstractyes

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