A systematic review of early neuroimaging and neurophysiological biomarkers for post-stroke mobility prognostication
Bibliographic record
Abstract
Abstract Background Accurate prognostication of mobility outcomes is essential to guide rehabilitation and manage patient expectations. The prognostic utility of neuroimaging and neurophysiological biomarkers remains uncertain when measured early post-stroke. This systematic review aimed to examine the prognostic capacity of early neuroimaging and neurophysiological biomarkers of mobility outcomes up to 24-months post-stroke. Methods MEDLINE and EMBASE were searched from inception to June 2025. Cohort studies that reported neuroimaging or neurophysiological biomarkers measured ≤14-days post-stroke and mobility outcome(s) assessed >14-days and ≤24-months post-stroke were included. Biomarker analyses were classified by statistical analysis approach (association, discrimination/classification or validation). Magnitude of relevant statistical measures was used as the primary indicator of prognostic capacity. Risk of bias was assessed using the Quality in Prognostic Studies tool. Meta-analysis was not performed due to heterogeneity. Results Twenty reports from 18 independent study samples (n=2,160 participants) were included. Biomarkers were measured a median 7.5-days post-stroke, and outcomes were assessed between 1- and 12-months. Eighty-six biomarker analyses were identified (61 neuroimaging, 25 neurophysiological) and the majority used an association approach (88%). Few used discrimination/classification methods (11%), and only one conducted internal validation (1%); an MRI-based machine learning model which demonstrated excellent discrimination but still requires external validation. Structural and functional corticospinal tract integrity were frequently investigated, and most associations were small or non-significant. Lesion location and size were also commonly examined, but findings were inconsistent and often lacked magnitude reporting. Methodological limitations were common, including small sample sizes, moderate to high risk of bias, poor reporting of magnitudes, and heterogeneous outcome measures and follow-up time points. Conclusions Current evidence provides limited support for early neuroimaging and neurophysiological biomarkers to prognosticate post-stroke mobility outcomes. Most analyses remain at the association stage, with minimal progress toward validation and clinical implementation. Advancing the field requires international collaboration using harmonized methodologies, standardised statistical reporting, and consistent outcome measures and timepoints. Registration URL: https://www.crd.york.ac.uk/prospero/ ; Unique identifier: CRD42022350771.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".