Relearning lost vocabulary in nonfluent progressive aphasia with MossTalk Words®
Bibliographic record
Abstract
Background: The literature on aphasia has been growing rapidly, with reports of different therapeutic approaches for a post‐stroke anomia. While individuals with post‐stroke anomia frequently recover to some extent, the other end of the aphasia recovery continuum is occupied by those who experience relentless language dissolution as a result of progressive disorders such as primary progressive aphasia. One of the most recent additions to the field of aphasia rehabilitation is therapy whereby either part of or the entire therapy is administered via computer‐based programmes. There have been few treatment studies investigating the rehabilitation of language abilities in people with primary progressive aphasia (PPA). Aims: The objectives of this investigation were to examine the ability of PPA individuals to relearn lost words and to determine the extent of benefits derived from MossTalk Words®, a computer‐based treatment for anomia. Methods and Procedures: Using a multiple baseline across behaviours design, we explored treatment‐specific effects, maintenance, and generalisation of improvements derived from this therapy programme. Two participants with nonfluent PPA were treated, each on three lists of words for which low and stable baselines were first established. Sessions occurred two to three times a week. Treatment involved the presentation of a picture on the computer screen, with the participants being required to name it. Success in treatment was measured by probing list naming every second session. Once a participant attained 80% accuracy over two consecutive probes, or participated in 12 sessions (whichever occurred first), treatment of a list was terminated and the next list was started. Each participant was tested on all items immediately after therapy, and again 1 month later. Outcomes and Results: Both participants improved their naming skills with the MossTalk Words®. P1 required only four sessions to reach the proposed criterion of 80% (up to 100%) correct on each list. The effects of treatment were maintained immediately and, to a lesser degree, 4 weeks later. P2 required all 12 sessions for each of the three lists. Results were variable immediately after testing, but seemingly maintained 4 weeks later. Conclusions: The results demonstrate that both participants with primary progressive aphasia benefited (although to a different extent) from a computer‐based treatment for anomia. These results are encouraging and suggest that such a treatment may be a viable therapy approach for patients who suffer from PPA in the absence of a generalised cognitive impairment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".