RecoverNow: Feasibility of a Mobile Tablet-Based Rehabilitation Intervention to Treat Post-Stroke Communication Deficits in the Acute Care Setting
Bibliographic record
Abstract
BACKGROUND: Approximately 40% of patients diagnosed with stroke experience some degree of aphasia. With limited health care resources, patients' access to speech and language therapies is often delayed. We propose using mobile-platform technology to initiate early speech-language therapy in the acute care setting. For this pilot, our objective was to assess the feasibility of a tablet-based speech-language therapy for patients with communication deficits following acute stroke. METHODS: We enrolled consecutive patients admitted with a stroke and communication deficits with NIHSS score ≥1 on the best language and/or dysarthria parameters. We excluded patients with severe comprehension deficits where communication was not possible. Following baseline assessment by a speech-language pathologist (SLP), patients were provided with a mobile tablet programmed with individualized therapy applications based on the assessment, and instructed to use it for at least one hour per day. Our objective was to establish feasibility by measuring recruitment rate, adherence rate, retention rate, protocol deviations and acceptability. RESULTS: Over 6 months, 143 patients were admitted with a new diagnosis of stroke: 73 had communication deficits, 44 met inclusion criteria, and 30 were enrolled into RecoverNow (median age 62, 26.6% female) for a recruitment rate of 68% of eligible participants. Participants received mobile tablets at a mean 6.8 days from admission [SEM 1.6], and used them for a mean 149.8 minutes/day [SEM 19.1]. In-hospital retention rate was 97%, and 96% of patients scored the mobile tablet-based communication therapy as at least moderately convenient 3/5 or better with 5/5 being most "convenient". CONCLUSIONS: Individualized speech-language therapy delivered by mobile tablet technology is feasible in acute care.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".