P76 Fate of incomplete reperfusion caused by multiple small occlusions or slow flow in eTICI 2b reperfusion grade: insights from the ESCAPE-NA1 trial
Bibliographic record
Abstract
Introduction It remains unclear whether incomplete reperfusion caused by multiple small occlusions or slow flow after mechanical thrombectomy (MT) results in infarction on follow-up imaging.1 Aim We investigated whether small peripheral thrombi or slow flow was associated with infarction on follow-up scan. Methods Patients from the ESCAPE-NA1 trial with final reperfusion grade eTICI 2b were analysed. Residual/peripheral occlusions were evaluated on the final DSA run. Patients with multiple small non-MT-accessible occlusions (clear marked filling defects representing the peripheral thrombus edge (meniscus sign)) or slow flow (gradual stagnation of flow) or both (mixed pattern) were included. Established infarct within the incomplete reperfusion territory was retrospectively reviewed on baseline non-contrast CT to distinguish slow flow through infarcted brain. New infarct in the incomplete reperfusion territory was assessed on follow-up CT/MR. Results Of 1105 ESCAPE-NA1 patients, 443 with final eTICI 2b reperfusion were included (40.1%). Of these, 260 had multiple small non-MT-accessible occlusions. Meniscus sign was detected in 26/260 (10%) cases, slow flow in 156/260 (60%) and mixed pattern in 78/260 (30%). 47/260 cases (18.1%) had an established infarct on baseline imaging. Of the remaining 213 cases, IR resulted in infarction in 114 cases (43.9%); of which, meniscus sign was seen in 16/114 cases (14%) and mixed pattern in 42/114 cases (36.8%). Conclusion The majority of incomplete reperfusion caused by multiple small occlusions or slow flow was associated with new infarction on follow-up imaging. In half of these cases, distal occlusions (meniscus sign) were detected, which could be potential targets for intra-arterial thrombolysis. References Kaesmacher J, Ospel JM, Meinel TR, et al. Thrombolysis un Cerebral Infarction 2b Reperfusions. Stroke. September 2020. Do you have any conflict of interest to declare?: No
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".