Abstract 1: Antegrade Flow Across an Intracranial Occlusion Can be Reliably Assessed on Ct Perfusion Source Images and it Predicts Recanalization With Intravenous Tpa
Bibliographic record
Abstract
Background: We aimed to see if antegrade flow observed on CT Perfusion Source Images (CTPSi) across an intracranial occlusion correlated with first run of DSA and predicted recanalization on DSA or repeat CTA. Methods: Patients with acute ischemic stroke and large vessel intracranial occlusion on CTA who had a CT Perfusion study followed by DSA or repeat CTA 4-6 hours later were included. CT Perfusion parameters were 8 cm coverage in static mode, acquisitions at 5 mm thickness, 5 seconds delay after contrast & 24 passes over 66 seconds. Antegrade flow was defined as the presence of ‘clot enhancement’ sign on the 1st pass of CTPSi and increasing density of contrast permeating the clot and filling the vessel distal to the occlusion on the 2nd and 3rd passes of CTPSi (Fig 1 & 2). This was correlated with the first run of DSA and recanalization was assessed on DSA (Group 1) or repeat CTA (Group 2). Results: Total 56 patients were included. In group 1(n=35), antegrade flow on CTPSI was present in 14/35 patients (40%). All these patients had antegrade flow on DSA and 12 of them showed early recanalization (TICI 2a, 2b or 3). IV t-PA was received by 29/35 patients. The sensitivity and specificity of CTPSi to predict antegrade flow when compared to DSA was 86.7% (95% CI, 59.5 - 98.3) and 95% (95% CI, 75.1 - 99.9) respectively. In Group 2 (n=21), antegrade flow was seen on CTPSI in 13 patients (62%) and all of them recanalized with IV t-PA. Six out of 8 patients without antegrade flow on CTPSi did not recanalize. Conclusion: Antegrade flow across an occlusion can be reliably assessed on initial passes of CTPSi and it predicts recanalization with IV t-PA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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".