COGNITIVE TRAINING FOLLOWING STROKE: A PILOT STUDY WITH THE NEUROAIREH@B PLATFORM
Bibliographic record
Abstract
Resumo: O treino cognitivo (TC) através das novas tecnologias representa uma estratégia de intervenção promissora na mitigação dos défices cognitivos pós-AVC.Neste estudo-piloto, avaliamos o impacto a curto prazo de um novo sistema de TC com maior validade ecológica -a plataforma NeuroAIreh@b -, numa amostra de sobreviventes de AVC na fase crónica.Recrutámos dez sobreviventes de AVC que foram submetidos a uma avaliação neuropsicológica (ANP) préintervenção.Posteriormente, iniciaram uma intervenção de TC implementada via tablet, com recurso à versão protótipo da plataforma NeuroAIreh@b, envolvendo oito sessões de 45 minutos.Nestas sessões, realizaram quatro tipos de tarefas de TC baseadas em atividades de vida diária (AVDs) (por ex., selecionar os ingredientes corretos para fazer uma receita, pagar as compras no supermercado).Foram efetuadas ANPs pós-intervenção para avaliar o impacto da intervenção a curto prazo.Uma análise intra-grupal com o teste de Wilcoxon revelou diferenças estatisticamente significativas no Montreal Cognitive Assessment (MoCA) e na pontuação total do Inventário de Avaliação Funcional de Adultos e Idosos (IAFAI).Globalmente, o TC através da plataforma NeuroAIreh@b parece ser benéfico na fase crónica do AVC, conduzindo a ganhos na cognição geral (MoCA) e na capacidade funcional (IAFAI).Estes resultados preliminares com a versão protótipo da plataforma NeuroAIreh@b são encorajadores e sugerem a generalização dos ganhos obtidos em contexto terapêutico para as AVDs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".