Correlation between Alberta Stroke Program Early Computed Tomography Score (ASPECTS) and National Institute of Health Stroke Score (NIHSS) in Ischemic Stroke
Bibliographic record
Abstract
Computed tomography scanning (CT-scan) hold an important role in diagnosing ischemic stroke, but may find difficulties to assess an early ischemic changes. ASPECTS provide a tool for assessing CT-scan in ischemic stroke, which can be used as predictor of stroke outcome. Stroke outcome and severity can also be assessed using NIHSS. We hypothesize that ASPECT score had negative correlation with neurological deficit in patient with acute ischemic stroke compared with non-contrast head CT scan. This was an analytic-descriptive cross-sectional study on firstonset ischemic stroke patient in Neurology Ward of Hasan Sadikin General Hospital admitted from October 2017 -February 2018. ASPECTS was calculated from CT-scan of ischemic stroke patients involving medial cerebral artery and compared to NIHSS. From 58 subject (44.8% male, 55.2%) female), with mean age 56.60+ 9.1 years, there were 58.6% subject with lacunar stroke, 20.7% with large artery atherosclerotic (LAA) stroke, and 20.7% with cardioemboli stroke. Subjects with LAA stroke and lacunar stroke had higher ASPECTS (p value < 0.05) and had lower NIHSS (p value < 0.05) than subjects with cardioembolic stroke. Spearman's correlation test between ASPECTS and NIHSS show a strong correlation between ASPECTS and NIHSS (r=-0.680, p<0.001). There was a strong inverse correlation between ASPECTS and NIHSS score on acute ischemic stroke. The higher the value of ASPECTS, the lower the value will be for NIHSS and ASPECT score had correlation with stroke severity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".