Comparing CST Lesion Metrics as Biomarkers for Recovery of Motor and Proprioceptive Impairments After Stroke
Bibliographic record
Abstract
Background. Corticospinal tract (CST) damage is considered a biomarker for stroke recovery. Several methods have been used to define CST damage and examine its relationship to motor performance, but which method is most useful remains unclear. Proprioceptive impairment also affects stroke recovery and may be related to CST damage. Methods. Robotic assessment quantified upper-limb motor and proprioceptive performance at 2 weeks and 6 months poststroke (n = 149). Three previously-established CST lesion metrics were calculated using clinical neuroimaging. Diffusion magnetic resonance imaging quantified CST microstructure in a subset of participants (n = 21). Statistical region of interest (sROI) analysis identified lesion locations associated with motor and proprioceptive deficits. Results. CST lesion metrics were moderately correlated with motor scores at 2 weeks and 6 months poststroke. CST fractional anisotropy (FA) was correlated with motor scores at 1 month poststroke, but not at 6 months. The FA ratio of the posterior limb of the internal capsule was not correlated with motor performance. CST lesion metrics were moderately correlated with proprioceptive scores at 2 weeks and 6 months poststroke. sROI analysis confirmed that CST damage was associated with motor and proprioceptive deficits and additionally found that putamen, internal capsule, and corticopontocerebellar tract lesions were associated with poor motor performance. Conclusions. Across all methods used to quantify CST damage, correlations with motor or proprioceptive performance were moderate at best. Future research is needed to identify complementary or alternative biomarkers to address the complexity and heterogeneity 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".