MétaCan
Menu
Back to cohort
Record W2050113674 · doi:10.1080/02687030801943005

Relearning lost vocabulary in nonfluent progressive aphasia with MossTalk Words®

2008· article· en· W2050113674 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAphasiology · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of OttawaToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsAphasiaPrimary progressive aphasiaPsychologyRehabilitationVocabularyStroke (engine)Session (web analytics)Cognitive psychologyPhysical medicine and rehabilitationMedicineLinguisticsComputer scienceNeuroscienceDementia

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.267
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it