Predictors of functional outcome after thrombectomy for M2 occlusions: a large scale experience from clinical practice
Bibliographic record
Abstract
Mechanical thrombectomy (MT) for acute ischemic stroke with medium vessel occlusions is still a matter of debate. We sought to identify factors associated with clinical outcome after MT for M2-occlusions based on data from the German Stroke Registry-Endovascular Treatment (GSR-ET). All patients prospectively enrolled in the GSR-ET from 05/2015 to 12/2021 were analyzed (NCT03356392). Inclusion criteria were primary M2-occlusions and availability of relevant clinical data. Factors associated with excellent/good outcome (modified Rankin scale mRS 0-1/0-2), poor outcome/death (mRS 5-6) and mRS-increase pre-stroke to day 90 were determined in multivariable logistic regression. 1348 patients were included. 1128(84%) had successful recanalization, 595(44%) achieved good outcome, 402 (30%) had poor outcome. Successful recanalization (odds ratio [OR] 4.27 [95% confidence interval 3.12-5.91], p < 0.001), higher Alberta stroke program early CT score (OR 1.25 [1.18-1.32], p < 0.001) and i.v. thrombolysis (OR 1.28 [1.07-1.54], p < 0.01) increased probability of good outcome, while age (OR 0.95 [0.94-0.95], p < 0.001), higher pre-stroke-mRS (OR 0.36 [0.31-0.40], p < 0.001), higher baseline NIHSS (OR 0.89 [0.88-0.91], p < 0.001), diabetes (OR 0.52 [0.42-0.64], p < 0.001), higher number of passes (OR 0.75 [0.70-0.80], p < 0.001) and intracranial hemorrhage (OR 0.26 [0.14-0.46], p < 0.001) decreased the probability of good outcome. Additional predictors of mRS-increase pre-stroke to 90d were dissections, perforations (OR 1.59 [1.11-2.29], p < 0.05) and clot migration, embolization (OR 1.67 [1.21-2.30], p < 0.01). Corresponding to large-vessel-occlusions, younger age, low pre-stroke-mRS, low severity of acute clinical disability, i.v. thrombolysis and successful recanalization were associated with good outcome while diabetes and higher number of passes decreased probability of good outcome after MT in M2 occlusions. Treatment related complications increased probability of mRS increase pre-stroke to 90d.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".