Mobile tablet-based therapies following stroke: a systematic scoping review protocol of attempted interventions and the challenges encountered
Bibliographic record
Abstract
BACKGROUND: Stroke is a growing global epidemic limiting the ability of millions to function independently due to post-stroke deficits and complications. Although specialized stroke rehabilitation improves the recovery of functional abilities, accessing rehabilitation services has become increasingly challenging as the number of stroke survivors continues to increase and rehabilitation resources remain scarce. Mobile tablet-based therapies (MTBTs) may be a resource-efficient platform for providing stroke rehabilitation services. The feasibility and challenges of offering MTBTs to stroke survivors should be well understood before expensive, large-scale clinical trials are undertaken to study treatment efficacy. METHOD: A systematic scoping review will be conducted to describe attempted MTBTs following stroke and the challenges encountered by survivors and study staff. Studies of interest will evaluate MTBTs offered to adult stroke patients in response to post-stroke complications or deficits. Journal databases, gray literature sources, clinical trial registries, relevant organizational websites, and reference lists of eligible studies will be searched to identify suitable studies. Study characteristics, barriers to care, methodological challenges, patient-reported outcomes, and health outcomes will be extracted to describe MTBTs and understand the challenges encountered in context. Results will be presented using descriptive statistics, tables, figures, and narrative description to summarize the scope of the field. DISCUSSION: Trends in MTBT feasibility and common challenges will be discussed to summarize major findings and highlight research gaps. Solutions to common challenges experienced by intervention participants and study staff will be proposed. Implications for the conduct of randomized clinical trials of MTBT efficacy and the appropriateness of a systematic review and meta-analysis of completed trials will be discussed. SYSTEMATIC REVIEW REGISTRATION: uO Research ( http://hdl.handle.net/10393/35696 ).
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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 | 0.103 | 0.099 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.010 |
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".