Alberta Stroke Program Early CT Score can Predict Severity of Spasticity and Functional Outcome in Ischemic Stroke Survivors
Bibliographic record
Abstract
Background: Post-stroke spasticity is common and an early predictor of the severity of spasticity can help track recovery trajectory helping to modify rehabilitation plans. Objectives: We explored the utility of the Alberta Stroke Program Early CT Score (ASPECTS) to predict functional motor capacity in patients after acute ischemic stroke. Methods: One hundred and one patients (mean age of 58.6 ± 7.6 years; M:F = 72: 29) with the first documented acute ischemic stroke were followed up for three to twelve months after the stroke. Cerebral lesions within the territory of the middle cerebral artery were evaluated using the ASPECTS. Spasticity was assessed using the Modified Ashworth Score (MAS) and walking with Timed Up and Go test (TUG). The associations between severity of spasticity and size/extent of infarct as derived from ASPECTS and between spasticity and functional walking in post-stroke survivors were analyzed. Results: Among the patients studied, 61.3% (n = 62) had infarct in the region of supply of the left middle cerebral artery (MCA) and 38.7% (n = 39) had infarct in the region supplied by the right MCA. Three percent (n = 3) had a low ASPECTS, 53.6% (n = 54) had an intermediate score and 44.4% (n = 44) had a high score. The majority of patients with no to mild spasticity had high ASPECTS. Worse spasticity was significantly associated with low ASPECTS (P = 0.001). High scores in Timed Up and Go test (TUG) were associated with low ASPECTS (P < 0.001). Patients with high ASPECTS had the propensity to have subcortical infarcts (P < 0.001) when compared to those with moderate ASPECTS, who had a mix of cortical and subcortical infarcts. Conclusion: ASPECTS at admission in patients with acute ischemic stroke is a good predictor of post-stroke spasticity and functional walking. Low ASPECTS is associated with higher spasticity and lower functional walking status on follow-up after stroke.
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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 | 0.003 |
| 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) | 0.002 | 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".