Smartphone use as an efficient tool to improve anomia in primary progressive aphasia
Bibliographic record
Abstract
Cognitive interventions are helpful in the non-pharmacological management of Primary progressive aphasia (PPA) and other neurodegenerative disorders of cognition, by helping patients to compensate for their cognitive deficits and improve their functional independence. In this study, we examined the effectiveness of cognitive rehabilitation based on the use of mobile device technology in PPA. The aim of this research study was to determine if BL, a patient with semantic variant PPA (svPPA) and severe anomia, was able to learn using specific smartphone functions and an application to reduce her word finding difficulties. She was trained during the intervention sessions on a list of target pictures to measure changes in picture naming performance. Errorless learning was applied during learning. BL quickly learned to use smartphone functions and the application over the course of the intervention. She significantly improved her anomia for trained pictures, and to a lesser extent for untrained semantically related pictures. Picture naming performance was maintained six months after the intervention, and she continued to use her smartphone regularly to communicate with family members and friends. This study confirms that smartphone use can be learned in PPA, which can help reduce the symptoms of anomia and improve communication skills.
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".