Relevance of Non-Contrast Computed Tomography (NCCT) Based Alberta Stroke Program Early CT Score (ASPECTS) in Predicting Severity of Acute Ischemic Stroke at Presentation and Its Functional and Cognitive Outcome at 90 Days
Bibliographic record
Abstract
Introduction: ASPECTS is a NCCT based topographic scoring system that provides quantitative measure of early ischemic changes. The score was initially developed for evaluating candidacy for stroke thrombolysis but currently also predicts functional and cognitive outcomes of stroke. Methods: 35 patients with acute ischemic stroke presenting within 48 hours of onset were included in the study. NIHSS score was ascertained at presentation and ASPECTS score was calculated (less than 6 and 6 or greater). On presentation NIHSS score and length of hospital stay were considered to be markers of early severity and mRS and MOCA scores were assessed at 90 days. Patients with MoCA less than 26 were considered to be having post stroke cognitive impairment. Results: Correlation between ASPECTS and NIHSS, stay length, 90-day mRS and MoCA were -0.452, -0.632, -0.778, 0.618 respectively. ASPECTS of less than 6 by univariate analysis was seen to be a risk factor for more severe strokes in acute setting with greater morbidity and cognitive decline at 90 days. Cardioembolic strokes also tended to have greater post stroke cognitive decline. Discussion: Poorer ASPECTS score at admission had greater stroke severity in acute phase and has worse long-term outcomes both in terms of functional and cognitive impairment and a cut off of less than 6 can be considered for the same. Conclusion: ASPECTS score is a surrogate marker of early and long-term stroke severity and its impacts.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".