Baseline ASPECTS and e‐ASPECTS Correlation with Infarct Volume and Functional Outcome in Patients Undergoing Mechanical Thrombectomy
Bibliographic record
Abstract
ABSTRACT BACKGROUND AND PURPOSE The role of Alberta Stroke Program Early CT score (ASPECTS) in predicting which patients are likely to benefit from endovascular therapy (EVT) is not well defined. An automated software (e‐ASPECTS) has been created to solve its poor interrater reliability. We aim to evaluate correlation between radiologist (Rx) and e‐ASPECTS scoring with cerebral blood volume (CBV) infarct core and with final infarct volume; as well as with long‐term functional outcome. METHODS We included patients with acute ischemic stroke and large vessel occlusion who underwent EVT. We measured baseline radiologist (Rx) ASPECTS and e‐ASPECTS, and baseline CBV infarct core on CT perfusion. Final infarct volume was measured on 24‐hour control CT. RESULTS We included 184 patients, in which 82.1% of patients achieved complete recanalization. Median Rx‐ASPECTS/e‐ASPECTS was 9 (IQR 8–10 vs. IQR 7.75‐10) and mean CBV lesion was 29.51 (±47.41) mL. Correlation (rs) between ASPECTS and e‐ASPECTS was .44 (P < .01). Both ASPECTS scores correlated with CBV after 180 minutes of symptom onset (rs = –.41 vs. –.54, P < .01) and with final infarct volume in patients with complete recanalization (rs = –.40 vs. –.43, P < .01). In a logistic regression, either Rx‐ASPECTS, e‐ASPECTS, and CBV (OR 1.60 vs. 1.87 vs. .96; P < .05) predicted a low infarct volume. Rx‐ASPECTS and e‐ASPECTS also predicted functional independence (mRS 0–2) at 3 months (1.52 vs. 1.37; P < .05). CONCLUSION ASPECTS and e‐ASPECTS showed a mild correlation with CBV. Rx‐ASPECTS, e‐ASPECTS, and CBV predicted a low infarct volume after thrombectomy in recanalized patients but only Rx‐ASPECTS and e‐ASPECTS predicted functional independence at 3 months.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".