Correlation of Motor and Functional Recovery with Neuroimaging in Ischaemic Stroke: An Observational Study
Bibliographic record
Abstract
Introduction: Stroke is a leading cause of long-term disability worldwide. Neuroimaging plays a critical role in diagnosing and planning the treatment of stroke. Early prognostic markers help in predicting the prognosis after stroke. Aim: To correlate the motor recovery and functional outcome with Computed Tomography (CT) brain findings using Alberta Stroke Programme Early Computed Tomography Score (ASPECTS) in patients with ischaemic stroke. Materials and Methods: This observational cross-sectional study was conducted in Department of Physical Medicine and Rehabilitation at Vardhman Mahavir Medical College and Safdarjung Hospital (tertiary care centre), from October 2016 to March 2018. A total of 45 patients diagnosed with Middle Cerebral Artery (MCA) territory Ischaemic Stroke were included. Motor and functional assessment were done using Fugl-Meyer Assessment (FMA), and Barthel Index (BI). FMA and BI were correlated with the radiological assessment using ASPECTS on Non Contrast Computed Tomography (NCCT) of head. Results: The mean age of the study population was 60.98±8.61 years. The mean BI score was 70.34± 25.2, and FMA score was 61.56±32.8. The mean ASPECTS was 7.5, with 26 patients having ASPECTS 8-10 and 19 with ASPECTS 0-7. Patients with higher ASPECTS (8-10) had moderate to no dependence in Activities of Daily Living (ADL) and mild to no motor impairment respectively (p-value<0.0001). Patients having lower ASPECTS (0-7) had severe dependency in ADL and motor impairment (p-value<0.0001). Conclusion: Alberta stroke programme early computed tomography score has a significant correlation with motor and functional recovery and is a strong predictor of outcome after ischaemic stroke. The higher the score on ASPECTS, the better will be the motor and functional outcome. The computed tomography findings of brain will help in stroke rehabilitation by enabling to set realistic goals at an early (acute) stage poststroke.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".