Impact of the ASPECT scores and distribution on outcome among patients undergoing thrombectomy for acute ischemic stroke
Bibliographic record
Abstract
INTRODUCTION: This study investigates whether the Alberta Stroke Program Early CT Score (ASPECTS) quantification is associated with outcome following mechanical thrombectomy. OBJECTIVE: To determine whether preintervention non-perfect ASPECT scores involving cortical or subcortical regions and the side of the non-perfect ASPECT score affects outcomes. METHODS: A retrospective review of a prospectively maintained database of patients with acute ischemic stroke involving the anterior circulation who underwent thrombectomy between May 2008 and August 2012 at a single tertiary care center. The device for mechanical thrombectomy used was the penumbra aspiration system (Penumbra Inc, Alameda, California, USA) and the Solitaire stent retriever (ev3, Irvine, California, USA). A 'blinded' neuroradiologist obtained ASPECTS quantification and noted each region demonstrating early changes. RESULTS: 149 patients (51.7% female, mean age 66.1±15.1 years) were included with an average National Institutes of Health Stroke Scale of 16.2±6.7. Patients with non-perfect ASPECT scores on pretreatment imaging were more likely to have a hemorrhagic conversion (p=0.04) evident on post-procedure CT. However, functional outcomes were the same. Patients with both cortical and basal ganglia non-perfect ASPECT scores were more likely to be in a persistent vegetative state or expire. No differences were identified in outcome among patients with left- versus right-sided infarcts affecting the basal ganglia or cortical regions. CONCLUSIONS: These findings support a strategy of selecting candidacy for thrombectomy that does not exclude patients with non-perfect ASPECT scores involving either the basal ganglia or cortical regions. Outcomes were identical among patients with no non-perfect ASPECT scores and those with cortical or subcortical infarcts, despite a higher incidence of hemorrhagic conversion found among those with non-perfect ASPECT scores.
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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 | 0.000 | 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".