Thrombus perviousness is not associated with first-pass revascularization using stent retrievers
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Recent studies suggest that thrombus imaging characteristics such as Hounsfield unit (HU) and perviousness assessed on noncontrast computed tomography (NCCT) and CT angiography (CTA) can predict successful recanalization. We assessed whether these thrombus imaging characteristics could predict successful first-pass recanalization. METHODS: We retrospectively reviewed cases of mechanical thrombectomy over a three-year period in which patients received a multiphase CTA and were treated with a stent retriever on first pass. Thrombus attenuation, thrombus enhancement on arterial- and delayed-phase CTA and percentage washout were calculated and their association with first-pass recanalization, successful recanalization and distal embolic complications was studied. RESULTS: Fifty-two mechanical thrombectomy patients were included. First-pass recanalization was achieved in 59.6% and complete revascularization (Thrombolysis in Cerebral Infarction scale 2b/3) was achieved in 84.6%. There was no correlation between first-pass recanalization with thrombus density on NCCT ( p = 0.94), percentage enhancement on arterial ( p = 0.61) and delayed-phase CTA ( p = 0.23) or thrombus length ( p = 0.16). There was no correlation between number of passes and either thrombus density on NCCT ( p = 0.91) or percentage enhancement on arterial- ( p = 0.79) and delayed-phase ( p = 0.14) CTA or thrombus length ( p = 0.34). Clot length was significantly higher in patients with distal embolic complications than in those without (18.5 ± 7.9 vs 11.4 ± 6.6 mm, p = 0.005). CONCLUSIONS: Our data suggest that thrombus imaging characteristics on multiphase CTA cannot predict first-pass recanalization or successful revascularization in acute ischemic stroke patients treated with stent retrievers. Longer clot length was associated with higher risk of distal embolic complications.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".