Lesion Characteristics of Individuals With Upper Limb Spasticity After Stroke
Bibliographic record
Abstract
This study explores the relationship between lesion location and volume and upper limb spasticity after stroke. Ninety-seven stroke patients (51 with spasticity) were included in the analysis (age = 67.5 ± 13.3 years, 57 males). Lesions were traced from computed tomography and magnetic resonance images and coregistered to a symmetrical brain template. Lesion overlays from the nonspastic group were subtracted from the spastic group to determine the regions of the brain more commonly lesioned in spastic patients. Similar analysis was performed across groups of participants whose upper limb (elbow or wrist) Modified Ashworth Scale (MAS) score ranged from 1 (mild) to 4 (severe). Following subtraction analysis and Fisher's exact test, the putamen was identified as the area most frequently lesioned in individuals with spasticity. More severe spasticity was associated with a higher lesion volume. This study establishes the neuroanatomical correlates of poststroke spasticity and describes the relationship between lesion characteristics and the severity of spasticity using mixed brain imaging modalities, including computed tomography imaging, which is more readily available to clinicians. Understanding the association between lesion location and volume with the development and severity of spasticity is an important first step toward predicting the development of spasticity after stroke. Such information could inform the implementation of intervention strategies during the recovery process to minimize the extent of impairment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".