Low (0–5) Alberta Stroke Program Early Computed Tomography Score on admission predictive of worse functional outcome after mechanical thrombectomy for anterior circulation large vessel occlusion
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We examined functional outcomes of mechanical thrombectomy (MT) procedures following anterior circulation large vessel occlusion (ACLVO)-related acute ischemic strokes (AIS). Results were based on admission non-contrast computed tomography (NCCT) studies, using the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) as standard metric. METHODS: Qualifying subjects were consecutive patients (N = 343) at a single center undergoing MT for ACLVO-related AIS. Each was grouped according to ASPECTS status on admission, determined from NCCT images by two physicians. Primary clinical endpoint was functional independence, assessed via modified Rankin Scale (mRS) at 90 days. Secondary endpoints were vessel recanalization (i.e., modified Thrombolysis in Cerebral Infarction [mTICI] score), symptomatic intracranial hemorrhage (sICH), and mortality. RESULTS: In this study population (mean age, 63.6 ± 12.6 years; women, 30.3%; median baseline National Institute of Health Stroke Scale [NIHSS] score, 15.2 ± 4.5), patients were stratified by ASPECTS tier at presentation, either 0-5 (n = 50) or 6-10 (n = 293). Multivariate logistic regression showed a relation between ASPECTS values ≤ 5 and lesser chance of 90-day functional improvement (OR = 2.309, 95% confidence interval [CI] 1.012-5.271; p = 0.047), once adjusted for age, baseline NIHSS score, diabetes mellitus, HbA1c concentration, D-dimer level, occlusive location, numbers of device passes, and successful recanalization. CONCLUSIONS: ASPECTS values ≤ 5 correspond with worse long-term functional improvement (mRS scores > 2) in patients undergoing MT for ACLVO-related AIS. Other independent determinants of functional outcomes after MT are age, baseline NIHSS score, HbA1c concentration, and successful recanalization.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".