Regional Contributions to Poststroke Disability in Endovascular Therapy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The relative contribution of each Alberta Stroke Program Early CT Score (ASPECTS) region to poststroke disability likely varies across regions. Determining the relative weights of each ASPECTS region may improve patient selection for endovascular stroke therapy (EST). METHODS: In the combined Solitaire Flow Restoration with the Intention for Thrombectomy (SWIFT), Solitaire Flow Restoration Thrombectomy for Acute Revascularization (STAR), and Solitaire Flow Restoration with the Intention for Thrombectomy as Primary Endovascular Treatment (SWIFT PRIME) databases, we identified patients treated with the Solitaire stent retriever. Using 24-h CT scan, a multivariate ordinal regression was used to determine the relative contribution of each ASPECTS region to clinical outcome separately in each hemisphere. The coefficients from the regression were used to create a weighted ASPECTS (wASPECTS), which was compared with the original ASPECTS to predict 90-day modified Rankin Scale disability outcomes in an independent validation cohort. RESULTS: Among 342 patients treated with EST, the average age was 67 years, 57% were female, and the median National Institutes of Health Stroke Scale (NIHSS) score was 17 (IQR 13-20). The median ASPECTS at presentation was 8 (IQR 7-10). The most commonly involved ASPECTS regions on 24-h CT were the lentiform nuclei (70%), insula (55%), and caudate (52%). In multivariate analysis, preservation of M6 (β = 9.7) and M4 (β = 4.4) regions in the right hemisphere was most strongly predictive of good outcome. For the left hemisphere, M6 (β = 5.5), M5 (β = 4.1), and M3 (β = 3.1) generated the greatest parameter estimates, though they did not reach statistical significance. A wASPECTS incorporating all 20 parameter estimates resulted in improved discrimination against the original ASPECTS in the independent cohort (C-statistic 0.78 vs. 0.67, right hemisphere). CONCLUSIONS: For both right and left hemisphere, preservation of the high cortical regions was more strongly associated with improved outcomes compared to the deep regions. Our findings support taking into consideration the location and relative weightings of the involved ASPECTS regions when evaluating a patient for EST.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".