Correlation between Clinical Outcomes and Baseline CT and CT Angiographic Findings in the SWIFT PRIME Trial
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Patient selection for endovascular therapy remains a great challenge in clinic practice. We sought to determine the effect of baseline CT and angiography on outcomes in the Solitaire With the Intention for Thrombectomy as Primary Endovascular Treatment for Acute Ischemic Stroke (SWIFT PRIME) trial and to identify patients who would benefit from endovascular stroke therapy. MATERIALS AND METHODS: The primary end point was a 90-day modified Rankin Scale score of 0-2. Subgroup and classification and regression tree analysis was performed on baseline ASPECTS, site of occlusion, clot length, collateral status, and onset-to-treatment time. RESULTS: = 44) (ASPECTS 6-7) (42% versus 21%; rate ratio, 1.98). The benefit of thrombectomy over IV tPA alone did not differ significantly by ASPECTS. Stratification by occlusion location also showed benefit with thrombectomy across all groups. Improved outcomes after thrombectomy occurred in patients with clot lengths of ≥8 mm (71% versus 43%; rate ratio, 1.67). Outcomes stratified by collateral status had a benefit with thrombectomy across all groups: none-fair collaterals (33% versus 0%), good collaterals (58% versus 44%), and excellent collaterals (82% versus 28%). Using a 3-level classification and regression tree analysis, we observed optimal outcomes in patients with favorable baseline ASPECTS, complete/near-complete recanalization (TICI 2b/3), and early treatment (mean mRS, 1.35 versus 3.73), while univariate and multivariate logistic regression showed significantly better results in patients with higher ASPECTS. CONCLUSIONS: While benefit was seen with endovascular therapy across multiple subgroups, the greatest response was observed in patients with a small baseline core infarct, excellent collaterals, and early treatment.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".