Cross‐Sectional Imaging Modalities in Correlation to the Thrombolysis in Cerebral Infarction Score: The Next Frontier in Adjunctive Endovascular Stroke Therapy
Bibliographic record
Abstract
The expanded Thrombolysis in Cerebral Infarction (eTICI) scale is the default method to measure reperfusion success of endovascular treatment for acute ischemic stroke. It is an estimate of the percentage of the vascular territory affected by the initial occlusion, or target downstream territory (TDT), that is reperfused after the intervention. Traditionally, the size of the TDT is determined on the preinterventional catheter angiography images by delineating the antegrade capillary deficit caused by the catheter angiography target occlusion. As such, the current definition of eTICI grading is only suitable to estimate the efficacy of reperfusion strategies occurring after the baseline catheter angiogram. However, reperfusion therapy for acute ischemic stroke due to large vessel occlusion often encompasses intravenous thrombolysis therapy started prior to endovascular treatment but after cross-sectional vascular imaging (computed tomography or magnetic resonance imaging) used to determine eligibility for endovascular treatment. The inherent shortcomings of the current eTICI scale to quantify preinterventional perfusion changes are discussed. We then argue that depending on the timing of the studied intervention - either between cross-sectional imaging and endovascular treatment or after first intracranial catheter angiography - the TDT used to determine the eTICI grade should be based on the occlusion as seen on admission cross-sectional vascular imaging or prethrombectomy catheter angiography, respectively. We propose a new conceptual framework to grade reperfusion based on the TDT derived from the occlusion seen on cross-sectional vascular imaging: the cross-sectional eTICI. Last, we discuss how this definition of the TDT more reliably measures preinterventional reperfusion and establishes homogenous definitions of embolization and infarctions in new territories.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".