Successful Reperfusion With Mechanical Thrombectomy Is Associated With Reduced Disability and Mortality in Patients With Pretreatment Diffusion-Weighted Imaging–Alberta Stroke Program Early Computed Tomography Score ≤6
Bibliographic record
Abstract
Background and Purpose— In acute ischemic stroke patients, diffusion-weighted imaging (DWI)–Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is correlated with infarct volume and is an independent factor of functional outcome. Patients with pretreatment DWI-ASPECTS ≤6 were excluded or under-represented in the recent randomized mechanical thrombectomy trials. Our aim was to assess the impact of reperfusion in pretreatment DWI-ASPECTS ≤6 patients treated with mechanical thrombectomy. Methods— We analyzed data collected between January 2012 and August 2015 in a bicentric prospective clinical registry of consecutive acute ischemic stroke patients treated with mechanical thrombectomy. Every patient with a documented internal carotid artery or middle cerebral artery occlusion with pretreatment DWI-ASPECTS ≤6 was eligible for this study. The primary end point was a favorable outcome defined by a modified Rankin Scale score ≤2 at 90 days. Results— Two hundred and eighteen patients with a DWI-ASPECTS ≤6 were included. Among them, 145 (66%) patients had successful reperfusion at the end of mechanical thrombectomy. Reperfused patients had an increased rate of favorable outcome (38.7% versus 17.4%; P =0.002) and a decreased rate of mortality at 3 months (22.5% versus 39.1%; P =0.013) compared with nonreperfused patients. The symptomatic intracranial hemorrhage rate was not different between the 2 groups (13.0% versus 14.1%; P =0.83). However, in patients with DWI-ASPECTS <5, favorable outcome was low (13.0% versus 9.5%; P =0.68) with a high mortality rate (45.7% versus 57.1%; P =0.38) with or without successful reperfusion. Conclusions— Successful reperfusion is associated with reduced mortality and disability in patients with a pretreatment DWI-ASPECTS ≤6. Further data from randomized studies are needed, particularly in patients with DWI-ASPECTS <5.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".