Prediction of stroke outcome in relation to Alberta Stroke Program Early CT Score (ASPECTS) at admission in acute ischemic stroke: A prospective study from tertiary care hospital in north India
Bibliographic record
Abstract
Objective: To evaluate correlation of Alberta Stroke Program Early CT Score (ASPECTS) and early and delay outcome measures among acute anterior ischemic stroke patients who presented within 48 hours of stroke onset. Methods: In a prospective cohort study, we recruited consecutive patients with acute middle cerebral artery (MCA) ischemic stroke who presented within 48 hours of stroke onset. All the patients were evaluated at admission (Glasgow Coma Scale - GCS and National Institute of Health Stroke Scale - NIHSS) at discharge (GCS, NIHSS, Barthel Index - BI and modifi ed Rankin Scale mRS) and at 3 months (BI and mRS). CT ASPECTS was calculated by two observers independently. We divided patients in to two groups with ‘Better’ and ‘Worse’ ASPECTS with score of 8-10 and 0-7 respectively and compared the primary and secondary stroke outcome measures. Results: Among 100 patients with acute MCA infarction (median age 55 yrs, 62 males), median ASPECTS scores had inter-rater reliability of 0.82. The mortality, GCS and NIHSS at discharge, and mRS and BI at 3 months are signifi cantly better among patients with ‘Better’ compared to ‘Worse’ APSECTS. The hospital stay was shorter in patients with Better ASPCTS. Conclusion: In the setting of acute ischemic stroke, ASPECTS has good correlation with severity of stroke, and is strong predictor of early and delayed outcome in acute ischemic 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".