Non-Contrast CT and CT-Angiogram for Late Window Ischemic Stroke Treatment Selection
Bibliographic record
Abstract
INTRODUCTION: The benefit of late window endovascular treatment (EVT) for anterior circulation ischemic stroke has been demonstrated using perfusion-based neuroimaging. We evaluated whether non-contrast CT (NCCT) and CT-angiogram (CTA) alone can select late-presenting patients for EVT. METHODS: We performed a retrospective comparison of all patients undergoing EVT at a single comprehensive stroke center from January 2016 to April 2017. Patients planned for EVT were divided into early (<6 hours from onset) and late (≥6 hours from onset or last time seen normal) window groups. Incidence of symptomatic hemorrhagic transformations (sHTs) at 24 hours and 3-month modified Rankin scores (mRSs) were compared. RESULTS: During the study period, 204 (82%) patients underwent EVT in the early and 44 (18%) in the late window. Median (interquartile range) NIH Stroke Scale Score was similar between groups (early: 18 [15-23] vs. late: 17 [13-21]), as were median ASPECT scores (early: 9 [8-10] vs. late: 9 [7-9]). In the late window, 42 (95%) strokes were of unknown onset. Similar proportions of sHT occurred at 24 hours (early: 12 [6%] vs. late: 4 [9%], p = 0.43). At 3 months, the proportion of patients achieving functional independence (mRS 0-2) were comparable in the early (80/192 [42%]) and late (16/41 [39%]) windows (p = 0.76). CONCLUSION: NCCT- and CTA-based patient selection led to similar functional independence outcomes and low proportions of sHT in the early and late windows. In centers without access to perfusion-based neuroimaging, this pragmatic approach could be safe, particularly for strokes of unknown onset.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".