Motor Rehabilitation Provides Modest Functional Benefits After Intracerebral Hemorrhage: a Systematic Review and Meta-Analysis of Translational Rehabilitation Studies
Bibliographic record
Abstract
Few certainties exist regarding the optimal type, timing, or dosage of rehabilitation after stroke. Despite differing injury mechanisms and recovery patterns following ischemic and hemorrhagic stroke, most translational stroke research is conducted after ischemia. As we enter the era of personalized medicine, exploring subtype-specific treatment efficacy is essential to optimizing recovery. Our objective was to characterize common rehabilitation interventions used after in vivo preclinical intracerebral hemorrhage (ICH) and assess the impact of post-ICH rehabilitation (vs. no-rehabilitation) on recovery of motor function. Following PRISMA guidelines, a systematic review (Academic Search Complete, CINAHL, EMBASE, Medline, PubMed Central) identified eligible articles published up to December 2022. Risk of bias (SYRCLE) and study quality (CAMARADES) were evaluated, and random-effects meta-analysis was used to assess treatment efficacy in recovery of forelimb and locomotor functions. Thirty articles met inclusion criteria, and 48 rehabilitation intervention groups were identified. Most used collagenase to model striatal ICH in young, male rodents. Aerobic exercise, enriched rehabilitation, and constraint-induced movement therapy represented ~ 70% of interventions. Study quality was low (median 4/10, range 2-8), and risk of bias was unclear. Rehabilitation provided modest benefits in skilled reaching, spontaneous impaired forelimb use, and locomotor function; however, effects varied substantially by endpoint, treatment type, and study quality. Rehabilitation statistically improves motor function after preclinical ICH, but whether these effects are functionally meaningful is unclear. Incomplete reporting and variable research quality hinder our capacity to analyze and interpret how treatment factors influence rehabilitation efficacy and recovery after ICH.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".