Impact of ASPECT scores and infarct distribution on outcomes among patients undergoing thrombectomy for acute ischemic stroke with the ADAPT technique
Bibliographic record
Abstract
OBJECTIVE: To investigate the associations between Alberta Stroke Program Early CT Score (ASPECTS) or distribution and sidedness of acute infarction and clinical outcomes following intervention with a direct aspiration first pass technique (ADAPT). METHODS: A review was performed of patients who had undergone thrombectomy with ADAPT for emergent large vessel occlusions of the middle cerebral artery (MCA) between December 2012 and May 2015. Preintervention CT scans were reviewed by a blinded radiologist to calculate ASPECTS and determine the distribution of infarction. Clinical outcomes were compared for subsets of patients depending upon ASPECTS and regional infarction distribution (cortical, subcortical, or both). RESULTS: One hundred and fifty-four patients (50% female, mean age 67) underwent thrombectomy using ADAPT for MCA emergent large vessel occlusion. The median presenting National Institute of Health Stroke Scale score was 15. Fifty-five per cent of patients had left-side occlusions. Similar good outcomes were achieved for patients with perfect and non-perfect ASPECTS (modified Rankin Scale (mRS) 0-2: 63% vs 51%, respectively; p=0.20). Similar outcomes were also achieved for patients with 'poor' ASPECTS (≤6) compared with those with ASPECTS >6 (mRS 0-2: 52% vs 53%, respectively; p=0.91). Regional distribution and sidedness of core infarction on preintervention CT also did not correlate with worse outcomes. CONCLUSIONS: Patients with moderate-sized core infarcts involving various distributions in either hemisphere can potentially achieve similar good outcomes compared with those with no core infarction at presentation. A treatment algorithm for acute ischemic stroke, which employs hardline ASPECTS thresholds or excludes patients with basal ganglia infarcts, might preclude patients who would potentially benefit from mechanical thrombectomy with ADAPT.
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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 | 0.005 |
| 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 | 0.000 | 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".