Communicative processes of individuals with injuries of the right cerebral hemisphere
Bibliographic record
Abstract
Objective: This study aimed to profile the language skills of patients with damage to the right cerebral hemisphere attended at the Ibirapuera Unit (Central) of the Association for the Welfare of Handicapped Children - AACD (Associação de Assistência à Criança Deficiente - AACD/Unidade Ibirapuera - Central), and to discover the perceptions of caregivers and patients regarding the presence or absence of language disorders after a stroke. Method: The descriptive study was conducted from July to September of 2009 with 11 adults through the application of the Montreal Communication Evaluation Battery (known in Brazil as Bateria MAC) tests, the Questionnaire on Awareness of Difficulties, and the Communicative Disorders Screening on individuals with neurological conditions, direct relatives, and/or caregivers. Results: It was found that 90.9% of patients with injuries of the right cerebral hemisphere had a deficit in at least one of the tests comprising the Bateria MAC assessment of language. Conclusion: Findings showing the patient’s absence of awareness of their linguistic and cognitive deficits are also very important, not only about their daily life activities, but about their agnosia.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".