Differential Contribution of ASPECTS Regions to Clinical Outcome after Thrombectomy for Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Ischemic stroke is the leading cause of long-term disability in adults, but our ability to prognosticate from baseline imaging data is limited. The ASPECTS measures ischemic change in the middle cerebral artery territory on noncontrast CT based on 10 anatomic regions. Here, we investigated whether infarction in particular regions was associated with worse long-term outcome. MATERIALS AND METHODS: We identified consecutive patients receiving mechanical thrombectomy for ICA/MCA occlusion at 2 comprehensive stroke centers. Pretreatment ASPECTS was assessed by 2 blinded reviewers. Clinical data including demographics, baseline NIHSS score, and 90-day mRS were collected. The relationship between individual ASPECTS regions and the mRS score (0-2 versus 3-6) was assessed using multivariable logistic regression. RESULTS: Three hundred fifty-three patients were included (mean age, 70 years; 46% men), of whom 214 had poor outcome (mRS = 3-6). Caudate (OR = 3.26; 95% CI, 1.33-8.82), M4 region (OR = 2.94; 95% CI, 1.09-9.46), and insula (OR = 1.75; 95% CI, 1.08-2.85) infarcts were associated with significantly greater odds of poor outcome, whereas M1 region infarction reduced the odds of poor outcome (OR = 0.38; 95% CI, 0.14-0.99). This finding remained unchanged when restricted to only patients with good recanalization. No significant associations were found by laterality. Similarly, no region was predictive of neurologic improvement during the first 24 hours or of symptomatic intracerebral hemorrhage. CONCLUSIONS: Our results indicate that ASPECTS regions are not equal in their contribution to functional outcome. This finding suggests that patient selection based on total ASPECTS alone might be insufficient, and infarct topography should be considered when deciding eligibility for thrombectomy.
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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 | 0.004 |
| 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) | 0.001 | 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".