Défices executivos após acidente vascular cerebral
Bibliographic record
Abstract
O objetivo principal deste estudo é identificar os principais défices executivos que ocorrem nos pacientes após o Acidente Vascular Cerebral (AVC). Método: Este estudo analisou 30 pacientes com défices de AVC comparando-os com 27 indivíduos saudáveis. Foi aplicada uma bateria de testes neuropsicológicos constituída pelo Montreal Cognitive Assessment (MoCA), o Ineco Frontal Screening (IFS), algumas sub-provas da Behavioral Assessment of Dysexecutive Syndrome (BADS) e o teste Stroop. Resultados: Verificou-se que os pacientes com AVC além de apresentarem piores resultados no screening cognitivo, demonstraram mais dificuldades em várias componentes do funcionamento executivo, nomeadamente, na iniciativa, planeamento, implementação, monitorização e adaptação do comportamento. Observou-se ainda uma lentificação na velocidade de processamento em algumas das tarefas executivas. No entanto não se verificaram diferenças entre os grupos a nível da programação motora e na memória de trabalho verbal. Conclusões: Os pacientes com AVC evidenciaram défices a nível da maioria das medidas do funcionamento executivo avaliadas, o que sugere a importância de avaliar e dirigir as estratégias de reabilitação neuropsicológica a este tipo de dificuldades cognitivas.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".