No changes in diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography score from before to after mechanical thrombectomy for anterior circulation occlusion are associated with good outcomes
Bibliographic record
Abstract
Abstract Background Although preoperative diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS) is well known as a predictor of outcomes after mechanical thrombectomy (MT) for large-vessel occlusion (LVO), assessment of changes in DWI-ASPECT from before to after MT is rare. Therefore, we clarified the relationship between the change in DWI-ASPECTS and clinical outcomes. Methods In this retrospective single-center study, we enrolled 63 cases of anterior LVOs treated with MT between April 2015 and March 2022. Preoperative and postoperative DWI-ASPECTSs were calculated. DWI-ASPECTSs were categorized into cortical-ASPECTSs (c-ASPECTSs) and subcortical ASPECTSs and assessed. Additionally, medical variables related to patients, such as sex, age, National Institutes of Health Stroke Scale (NIHSS) score, and premorbid modified Rankin Scale (mRS) score, were evaluated. A good outcome was defined as an mRS score of 0 or 2 at 3 months. Results Forty-five patients met the inclusion criteria. Among the patients, 9 (20%) had a good outcome. The good outcome group showed significantly higher postoperative DWI-ASPECTs (median 8 vs. 5, p = 0.012) and c-ASPECTSs (median 4 vs. 3, p = 0.020) than the not-good outcome group. No changes in DWI-ASPECTSs and c-ASPECTSs from before to after MT were significantly associated with the good outcome ( p = 0.017, p = 0.016, respectively). The cut-off values for the good outcome on receiver operating characteristic curve analysis for differences between DWI-ASPECTSs and c-ASPECTSs was 0 [area under the curve (AUC) 0.77, sensitivity 0.67, specificity 0.78] and 0 [AUC 0.74, sensitivity 0.44, specificity 1.00]. Logistic regression analyses showed that baseline NIHSS score (odds ratio, 0.69; 95% confidence interval 0.48–1.00; p = 0.046) and postoperative DWI-ASPECTS (odds ratio, 2.27; 95% confidence interval 1.02–5.04; p = 0.039) were independent factors for the good outcome. Conclusions The good outcome of patients with anterior LVO was not associated with any changes in DWI-ASPECTSs and c-ASPECTSs from before to after MT.
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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 | 0.002 |
| 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".