Treatment of verb anomia in aphasia: efficacy of self-administered therapy using a smart tablet
Bibliographic record
Abstract
Aphasia is a chronic condition that usually requires long-term rehabilitation. However, even if many effective treatments can be offered to patients and families, speech therapy services for individuals with aphasia often remain limited because of logistical and financial considerations, especially more than 6 months after stroke. Therefore, the need to develop tools to maximize rehabilitation potential is unquestionable. The aim of this study was to test the efficacy of a self-administered treatment delivered with a smart tablet to improve written verb naming skills in CP, a 63-year-old woman with chronic aphasia. An ABA multiple baseline design was used to compare CP's performance in verb naming on three equivalent lists of stimuli trained with a hierarchy of cues, trained with no cues, and not trained. Results suggest that graphemic cueing therapy, done four times a week for 3 weeks, led to better written verb naming compared to baseline and to the untrained list. Moreover, generalization of the effects of treatment was observed in verb production, assessed with a noun-to-verb production task. Results of this study suggest that self-administered training with a smart tablet is effective in improving naming skills in chronic aphasia. Future studies are needed to confirm the effectiveness of new technologies in self-administered treatment of acquired language deficits.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".