Standardizing Domains and Metrics of Stroke Recovery: A Systematic Review
Bibliographic record
Abstract
Background and Aims: Measuring stroke recovery poses a significant challenge, given the complexity of the recovery process. We aimed to identify a standardized and data-driven set of metrics of stroke rehabilitation in the literature that ensures the inclusion of all recovery domains and subdomains in the literature. Methods: A systematic review was conducted by four reviewers using the PRISMA guidelines on PubMed, MEDLINE, and Embase for stroke recovery articles between 2004 and 2024. The inclusion criteria comprised experimental/observational studies, including ischemic and hemorrhagic stroke. All studies had ≥20 participants who were ≥18 years of age, and had a follow-up of ≥3 months. Outcomes included demographics, geographic origin, stroke mechanism, domains and subdomains, metrics used, and follow-up. A bias assessment was performed using the Newcastle-Ottawa Scale and the Cochrane Risk of Bias 2.0 tool. This study was registered with PROSPERO (CRD42024551753). Results: Our search included 324 studies with a sample of 85,156 participants. The study identified seven domains (perception, physical and motor function (PF), speech and language (S&L), cognition, activities of daily living (ADL), quality of life (QoL), and social interaction) and 96 constituent subdomains that encompass the complete landscape of the stroke recovery literature identified. The domains of PF and ADL constituted the vast share of the literature, albeit reducing in their relative representation over time, while domains such as perception and QoL have been increasingly studied since 2004. Using the domains, the study identified the set and frequency of all commonly used metrics to measure stroke recovery in the literature, of which the NIHSS (n = 72), BI (n = 55), and mRS (n = 51) were the most commonly used. We identified eighteen standard metrics that ensure the inclusion of all seven domains and 96 subdomains. Summary of Review and Conclusions: The identified set of domains and metrics within this study can help inform further clinical research and decision-making by providing a standardized set of metrics to be used for each domain. This approach ensures lesser represented domains and subdomains are also included during testing, providing a more complete view and measure of stroke recovery.
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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 | 0.097 | 0.288 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".