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Preditores de incapacidade funcional em pessoas com acidente vascular cerebral isquêmico: dois a três anos do ictus

2025· article· pt· W4414345483 on OpenAlexaff
Brenda Silva Cunha, Mariana de Almeida Moraes, Larissa Alessandra Medeiros, Ludimila Santos Muniz, Carlos Antônio de Souza Teles Santos, Maria Cecília Bueno Jayme Gallani, Fernanda Carneiro Mussi

Bibliographic record

VenueRevista da Escola de Enfermagem da USP · 2025
Typearticle
Languagept
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsModified Rankin ScaleStroke (engine)Risk factorRetrospective cohort studyCohort

Abstract

fetched live from OpenAlex

RESUMO Objetivo: Analisar fatores clínicos e sociodemográficos associados à incapacidade funcional em pessoas com acidente vascular cerebral isquêmico (AVCI) entre 2 e 3 anos do evento. Método: Coorte prospectiva com 241 participantes. Foram utilizados instrumentos para caracterização sociodemográfica e clínica, a Escala de Rankin-m e um protocolo de ligação telefônica. Os dados foram analisados com testes Qui-quadrado de Pearson ou Exato de Fisher e Modelo de Poisson robusto, com significância de 5%. Resultados: Dos participantes, 62,6% apresentaram Rankin de 0 a 2. A análise multivariada mostrou que maior déficit neurológico (6–13 e ≥14) foi 2,35 (IC 95% 1,13;4,05) e 4,4 (IC 95% 2,09; 6,83) vezes mais associado a incapacidade moderada a grave; não realização de trombólise e recorrência do AVCi foram, respectivamente, 3,02 (IC 95%: 1,32;3,74) e 3,82 vezes (IC 95%: 1,49;3,47) mais associados a incapacidade moderada a grave. Conclusão: O evento impactou a capacidade funcional, com maior déficit neurológico, não realização de trombólise e recorrência do AVC como principais preditores de incapacidade. Estratégias como ampliação da trombólise, atendimento precoce, reabilitação contínua e controle de fatores de risco podem reduzir as incapacidades.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.301
Teacher spread0.284 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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