Abstract WP51: Mechanical Thrombectomy for Anterior Circulation Large Vessel Occlusion With Low DWI-ASPECTS
Bibliographic record
Abstract
Background: Even in acute anterior circulation large-vessel occlusion(LVO) patients with low Alberta Stroke Program Early Computed Tomography Score(ASPECTS), the DWI-FLAIR mismatch is considered to be a prognostic finding to obtain good outcomes. We performed mechanical thrombectomy for acute anterior circulation LVO with DWI-ASPECTS≤5 and DWI-FLAIR mismatch, and investigated prognostic factors. Methods: We retrospectively analyzed 25 consecutive patients with acute anterior circulation LVO with DWI-ASPECTS≤5 and DWI-FLAIR mismatch treated by mechanical thrombectomy from August 2014 to July 2018.We compared various clinical factors between patients with good outcome defined as modified Rankin Scale(mRS) 0-3 at discharge and poor outcome. Results: Nine(36%) of 25 patients had good outcomes: good outcomes were attained in 5 of 13 patients with ICA occlusion and 4 of 12 patients with M1 occlusion.Intravenous thrombolysis(IVT) was performed in 5 patients in good outcome group and 3 patients in poor outcome group.Successful recanalization(TICI2B-3) was obtained in 9(100%) patients of good outcome group and 14(87.5%) of poor outcome group.There was no symptomatic complication associated with mechanical thrombectomy and IVT in both groups.Baseline clinical characteristics including age, initial NIHSS and initial DWI-ASPECTS were similar between 2 groups.Onset-to-picture time was also similar between 2 groups (median [IQR], 109 min [63-127] vs. 110 [81-168]; p=0.58).Picture-to-puncture time and puncture-to-recanalization time were significantly shorter in patients with good outcomes compared with poor outcomes (77 min [60-82] vs. 94 [86-113], p=0.008; and 34 min [20-37] vs. 45 [32-60], p=0.047, respectively). Conclusions: To achieve a good outcome in mechanical thrombectomy for acute anterior circulation LVO with low DWI-ASPECTS and DWI-FLAIR mismatch, quick and complete recanalization may be mandatory.
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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 | 0.001 |
| 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".