Association between white matter integrity and lower limb motor impairment after stroke: A systematic review
Bibliographic record
Abstract
BACKGROUND: There is no clear consensus on the anatomical substrates required for recovery from lower limb (LL) impairment after stroke. Knowledge of biomarkers, such as white matter integrity (WMI), could fill this knowledge gap. OBJECTIVES: To analyze the associations between WMI of the corticospinal tract (CST) and corticoreticulospinal pathway (CRP) and LL motor impairment after stroke, in terms of synergistic control and muscle strength. It also explores whether any associations depend on time post-stroke. METHODS: In April 2023, PubMed, Web of Science, and Scopus databases were systematically searched for studies associating WMI of the CST and CRP and LL motor impairment after stroke. Risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scales. RESULTS: Nineteen studies were included, 15 about the association between CST and motor impairment, and 4 concerning CST and CRP. Associations were consistently found between higher WMI of the CST and greater muscle strength, but not with synergistic control. There were no clear associations between WMI of the CRP and muscle strength, and associations could not be analyzed for synergistic control. The results could not determine whether the associations are time dependent. CONCLUSIONS: The results of this review supported using WMI of the CST to understand LL muscle strength after stroke. However, the same cannot be said for LL synergistic control due to the small number of studies. There was no clear evidence of an association between WMI of the CRP and LL muscle strength or synergistic control due to mixed results or a lack of studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".