Abstract TP84: Agreement Between Alberta Stroke Program Early Computed Tomography Score and Computed Tomography Perfusion in Patient Selection for Mechanical Thrombectomy After Large Vessel Occlusion Acute Ischemic Stroke
Bibliographic record
Abstract
Introduction: CT ASPECTS and CT Perfusion (CTP) are used to select patients for mechanical thrombectomy, an evidence-based treatment for Large Vessel Occlusion (LVO) Acute Ischemic Stroke (AIS). However, discordant results between the two imaging modalities creates uncertainty with respect to the volume of ischemic and infarcted brain tissue, and thus whether to offer revascularization. We sought to investigate the agreement between CT ASPECTS and CTP in selecting patients for mechanical thrombectomy. Hypothesis: CT ASPECTS determined by a neuro-radiologist demonstrates moderate agreement with CTP in selecting patients with anterior circulation, LVO AIS for mechanical thrombectomy. Methods: Over a 7-month period beginning in January 2018, we conducted a retrospective analysis from a large healthcare system’s stroke network database comparing the agreement between favorable CT ASPECTS (defined as score ≥ 6) and favorable CTP. Favorable CTP was defined according to the inclusion criteria from EXTEND-IA, DEFUSE 3, and DAWN, in the 0-6 hour, 6-16 hour, and 6-24 hour time windows, respectively, for patients with ICA or proximal MCA occlusions. Results: Cases were identified in the 0-24 hour window with an ICA or M1 occlusion, baseline CT ASPECTS calculated by a neuro-radiologist, and CTP. The overall raw agreement between CT ASPECTS and CTP for the 145 cases in the 0-6 hour window was 81%, and Cohen’s kappa (κ) was 0.17 (no agreement). In the 6-16 hour window, the overall raw agreement for 46 cases was 78% (κ = 0.38, minimal agreement). In the 6-24 hour window, the overall raw agreement for 58 cases was 53% (κ = 0.14, no agreement). Conclusions: In both early and extended time windows, CT ASPECTS and CTP demonstrate minimal to no agreement beyond chance in patient selection for mechanical thrombectomy. Additional studies are required to determine the most appropriate imaging selection criteria to guide treatment decisions in patients with anterior circulation, LVO AIS.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".