Determining Corticospinal Tract Injury from Stroke Using Computed Tomography
Bibliographic record
Abstract
INTRODUCTION: Damage to the corticospinal tract (CST) from stroke leads to motor deficits. The damage can be quantified as the amount of overlap between the stroke lesion and CST (CST Injury). Previous literature has shown that the degree of motor deficits post-stroke is related to the amount of CST Injury. These studies delineate the stroke lesion from structural T1-weighted magnetic resonance imaging (MRI) scans, often acquired for research. In Canada, computed tomography (CT) is the most common imaging modality used in routine acute stroke care. In this proof-of-principle study, we determine whether CST Injury, using lesions delineated from CT scans, significantly explains the variability in motor impairment in individuals with stroke. METHODS: Thirty-seven participants with stroke were included in this study. These individuals had a CT scan within the acute stage (7 days) of their stroke and underwent motor assessments. Brain images from CT scans were registered to MRI space. We performed a stepwise regression analysis to determine the contribution of CST injury and demographic variables in explaining motor impairment variability. RESULTS: Using clinically available CT scans, we found modest evidence that CST Injury explains variability in motor impairment (R2adj = 0.12, p = 0.02). None of the participant demographic variables entered the model. CONCLUSION: We show for the first time a relationship between CST Injury and motor impairment using CT scans. Further work is required to evaluate the utility of data derived from clinical CT scans as a biomarker of stroke motor 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".