<p>Investigating the utility of teletherapy in individuals with primary progressive aphasia</p>
Bibliographic record
Abstract
INTRODUCTION: Primary progressive aphasia (PPA) is a neurodegenerative disorder characterized by progressive deterioration of speech and language. A growing body of research supports the utility of speech and language intervention in individuals with PPA, although access to these services remains limited. One potential means of increasing treatment accessibility is the delivery of treatment via telemedicine. Evidence supports the use of teletherapy in stroke-induced aphasia, but research examining the application of teletherapy in PPA is limited. In the current study, a non-randomized group comparison design was used to evaluate the feasibility and utility of treatment delivered via teletherapy relative to treatment administered in person for individuals with PPA. METHODS: Two treatment protocols were administered as part of a larger study investigating treatment for speech and language deficits in PPA. Participants with semantic (n=10) and logopenic (n=11) PPA received lexical retrieval treatment and individuals with nonfluent/agrammatic PPA (n=10) received video-implemented script training for aphasia designed to promote speech production and fluency. Treatment was administered via teletherapy for approximately half of the participants receiving each intervention. Treatment outcomes and performance on standardized tests were assessed at pre-treatment and post-treatment, as well as 3, 6, and 12 months post-treatment. RESULTS: Overall, both treatment approaches resulted in significant gains for primary outcome measures. Critically, comparison of in-person and teletherapy groups revealed comparable outcomes. Generalization to untrained targets and tasks and maintenance of treatment-induced gains were also comparable for traditional vs teletherapy participants. CONCLUSION: Overall, treatment outcomes were largely equivalent for individuals receiving treatment via teletherapy vs traditional, in-person delivery. Results support the application of teletherapy for administering restitutive interventions to individuals with mild-to-moderate PPA. Potential implications for using teletherapy in the treatment of cognitive-linguistic and motoric impairments in other disorders and suggestions for administering treatment via telemedicine are discussed.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".