Mechanical Thrombectomy in Anterior Circulation Occlusion Could Be More Effective than Medical Management Even in Low DWI-ASPECTS Patients
Bibliographic record
Abstract
The purpose of this study was to investigate whether patients with low preoperative Diffusion-weighted Imaging Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS) could benefit from mechanical thrombectomy for acute anterior circulation occlusion. This was a retrospective, non-blinded, cohort study. From September 2012 to August 2016, 83 consecutive patients of acute anterior circulation occlusion were treated with thrombectomy using second-generation devices or medical management. The DWI-ASPECTS was scored after the first MRI. Patient characteristics and clinical outcomes were compared between the treatment groups. Significant dependence was defined as a modified Rankin scale score ≥3 at 90 days. As a result, 33 patients underwent mechanical thrombectomy and 50 received medical management. In the mechanical thrombectomy group, the variable of lower DWI-ASPECTS (5, 4-6 vs. 8, 7-8, P < 0.001), especially ≤6, was significantly associated with poor prognosis. However, compared with patients of DWI-ASPECTS ≤ 6 who received medical management, there were significantly fewer patients with poor outcomes in thrombectomy (dependent in 11 of 15 vs. 23 of 23, respectively; P = 0.019). Although patients with lower pretreatment DWI-ASPECTS could benefit less from thrombectomy, their outcomes were still better than medical management. Therefore, mechanical thrombectomy could be considered in some patients with low pretreatment DWI-ASPECTS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".