E.2 Predictors of successful endovascular thrombectomy for M2 occlusion in acute ischemic stroke
Bibliographic record
Abstract
Background: There remains lack of data in regards to factors influencing successful endovascular reperfusion of isolated occlusion of the M2 segment of the middle cerebral artery (MCA). In this study, we set out to investigate the variables that affect the successful endovascular reperfusion of isolated M2 segment occlusion. Methods: M2 segment occlusion was defined as isolated clot anywhere within the M2 segment of the MCA. A prediction model of successful endovascular reperfusion defined as modified Thrombolysis in Cerebral Ischemia (mTICI) score of 2b, 2c and 3 and unsuccessful endovascular reperfusion defined as mTICI score of 0, 1 and 2a was developed from demographics (age, sex) , clinical factors (NIHSS at the time of presentation to hospital), imaging characteristics (ASPECTS, ICA occlusion, presence of Intracranial arterial disease (ICAD), computed tomography perfusion (CTP)-based ischemic core and mismatch volume estimation), and treatment (alteplase/tenecteplase use and periprocedural complications) variables from 64 patients who underwent endovascular thrombectomy (EVT) at Kingston Health Science Centre between December 24, 2018 and October 18, 2022. Results: The only statistical significant predictor of successful endovascular reperfusion was CTP-based ischemic core volume (smaller core volume) (p<0.05). Conclusions: The CTP-based ischemic core volume is the most important predictor of successful endovascular reperfusion for M2 occlusion.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".