Efficacy of a self‐administered treatment using a smart tablet to improve functional vocabulary in post‐stroke aphasia: a case‐series study
Bibliographic record
Abstract
BACKGROUND: Aphasia is an acquired language disorder that occurs secondary to brain injury, such as stroke. It causes communication difficulties that have a significant impact on quality of life and social relationships. Although the efficacy of speech-language therapy has been clearly demonstrated in this population, long-term services are currently limited due to logistical and financial constraints. In this context, the potential contribution of technology, such as smart tablets, is worth exploring, especially to improve vocabulary that is relevant in daily life. AIMS: The main aim was to investigate the efficacy of a self-administered treatment using a smart tablet to improve naming of functional words in post-stroke anomia. METHODS & PROCEDURES: Four adults with post-stroke aphasia took part in the study. An ABA design with multiple baselines was used to compare naming performances for four equivalent lists: (1) trained with functional words chosen with the participant; (2) trained with words randomly chosen from a picture database; (3) exposed but not trained; and (4) not exposed (control). OUTCOMES & RESULTS: For all participants, the treatment self-administered at home (four times/week for 4 weeks) resulted in a significant improvement for both sets of trained words that was maintained 2 months after the end of treatment. Moreover, in two participants, evidence of generalization to conversation was found. CONCLUSIONS & IMPLICATIONS: This study confirms the efficacy of using smart tablets to improve naming in post-stroke aphasia. Although more studies are needed, the use of new technologies is unquestionably a promising approach to improve communication skills in people with aphasia, especially by targeting vocabulary that is relevant to them in their daily lives.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".