Effect of word retrieval therapy on a patient with expressive aphasia: a case report
Bibliographic record
Abstract
ABSTRACT To verify the effect of word retrieval therapy on a patient with expressive aphasia. A forty-seven year-old, male, with 8 years of schooling, with complaints about not saying words after two ischemic stroke on the left hemisphere, participated in this study. The Montreal-Toulouse-Language Assessment Battery (MTL-BR), Brief Neuropsychological Assessment Instrument (NEUPSILIN-Af), Mini-Mental State Examination (MMSE) and Functional Assessment Communication Skills scale (ASHA-FACS) were used pre- and post-therapy. A baseline test with 50 words, 25 nouns and 25 verbs was applied to obtain data regarding naming ability. The sessions occurred twice a week, for 50 minutes. The intervention was based on a set of 25 images of nouns and verbs, in oral and written modalities during six sessions, for each category. On the three final sessions, 10 figures of nouns and 10 figures of verbs were added in sentences. In the post-therapy, the final baseline showed an increase in vocabulary of nouns and verbs. In the pos-intervention evaluation, the patient had an improvement in some tasks of MTL-BR battery, NEUPSILIN-Af tasks. Improvement in the social communication and daily planning aspects were reported in the ASHA-FACS. In conclusion, the word retrieval therapy was effective in this case, because there was an increase of the vocabulary and improvement in several linguistic, communicative and cognitive aspects.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".