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Record W1969905648 · doi:10.1177/1756285609336548

Implementation of computer-based language therapy in aphasia

2009· article· en· W1969905648 on OpenAlexafffund
Lisa M. D. Archibald, J. B. Orange, Donald J. Jamieson

Bibliographic record

VenueTherapeutic Advances in Neurological Disorders · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsWestern University
FundersCanadian Stroke Network
KeywordsAphasiaMedicineIntervention (counseling)ComprehensionSpeech therapyAudiologyPsychiatryLinguistics

Abstract

fetched live from OpenAlex

A first step in evaluating the use of computers in language therapy for individuals with aphasia is to establish the treatment as active in small groups prior to large-scale clinical trials. The present study evaluated a comprehensive computer-based language therapy program in a group of eight individuals with chronic in aphasia varying broadly in age, time post onset and aphasia type. Results revealed an overall therapeutic benefit in auditory comprehension, as well as positive trends in functional communication. Findings suggest that comprehensive therapy programs may be beneficial for many individuals with aphasia, and computer-based therapy may be one feasible avenue of providing this intervention.

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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.507

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.349
Teacher spread0.327 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2009
Admission routes2
Has abstractyes

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