Abstract W P43: Clot Characteristics on Ct-angiography Predict Early Recanalization With Intravenous Tpa in Patients With Acute Ischemic Stroke
Bibliographic record
Abstract
Objective: We explore relationships amongst known and novel clot characteristics identified on CTA and early recanalization with IV tPA using classification and regression tree analysis (CART). Methods: Data is from patients presenting with acute ischemic stroke and proximal anterior circulation occlusions from the Calgary CTA database (2003-2012) and the Keimyung Stroke Registry (2005-2009). Patients who received IV t-PA followed by endovascular therapy were included. Clot location, clot length, residual flow through clot, ratio of contrast HU pre/post clot (cirHU) and distance of clot from M1 origin (for M1 MCA occlusions), were assessed on baseline CT-angio using OsiriX (Fig 1). Early recanalization (TICI 2a, 2b & 3) with IV t-PA was assessed on DSA first run. Results: We identified 228 patients (50.4% male, median age 69 yrs, median baseline NIHSS 17) who fulfilled inclusion criteria. Median symptom onset to IV t-PA time was 120 mins (IQR=70 mins) and median IV t-PA bolus to first angio run time was 70.5 mins (IQR=62 mins). Patients with residual flow within clot are five times more likely to recanalize than those without. Patients with residual flow within clot and a shorter clot length (≤15mm) were the most likely to recanalize(70.6%). Patients without residual flow with a carotid T/L occlusion rarely recanalized (1.7%). Patients without residual flow in M1 clots recanalized more if they were distal and had a cirHU < 2 (36.8%). (Fig 2). Inter-rater reliability for these clot characteristics was good to excellent. Conclusion: Clot characteristics on CTA could help physicians estimate early recanalization rates with IV tPA for proximal clots ranging from 0% to more than 80%.
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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 | 0.001 | 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".