Improvement in functional vocabulary and generalization to conversation following a self-administered treatment using a smart tablet in primary progressive aphasia
Bibliographic record
Abstract
Currently, public services in speech-language pathology for primary progressive aphasia (PPA) are very limited, although several interventions have been shown to be effective. In this context, new technologies have the potential to enable people with PPA to improve their communication skills. The main aim of this study was to investigate the efficacy of a self-administered therapy using a smart tablet to improve naming of functional words and to assess generalization to an ecological conversation task. Five adults with PPA completed the protocol. Using an ABA design with multiple baselines, naming performance was compared across four equivalent lists: (1) trained with functional words; (2) trained with words from a picture database; (3) exposed but not trained; and (4) not exposed (control). Treatment was self-administered four times a week for a period of four consecutive weeks. A significant improvement for trained words was found in all five participants, and gains were maintained two months post-treatment in four of them. Moreover, in three participants, evidence of generalization was found in conversation. This study supports the efficacy of using a smart tablet to improve naming in PPA and suggests the possibility of generalization to an ecological context.
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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 | 0.000 | 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".