Pretreatment predictors of very poor clinical outcomes in medium vessel occlusion stroke patients treated with mechanical thrombectomy
Bibliographic record
Abstract
BACKGROUND: Acute ischemic stroke (AIS) from primary medium vessel occlusions (MeVO) is a prevalent condition associated with substantial morbidity and mortality. Despite the common use of mechanical thrombectomy (MT) in AIS, predictors of poor outcomes in MeVO remain poorly characterized. METHODS: In this prospectively collected, retrospectively reviewed, multicenter, multinational study, data from the MAD-MT (Multicenter Analysis of primary Distal medium vessel occlusions: effect of Mechanical Thrombectomy) registry were analyzed. The study included 1568 patients from 37 academic centers across North America, Asia, and Europe, treated with MT, with or without intravenous tissue plasminogen activator (IVtPA), between September 2017 and July 2021. RESULTS: Among the 1568 patients, 347 (22.2%) experienced very poor outcomes (modified Rankin score (mRS), 5-6). Key predictors of poor outcomes were advanced age (odds ratio (OR): 1.03; 95% confidence interval (CI): 1.02 to 1.04; p < 0.001), higher baseline National Institutes of Health Stroke Scale (NIHSS) scores (OR: 1.07; 95% CI: 1.05 to 1.10; p < 0.001), pre-operative glucose levels (OR: 1.01; 95% CI: 1.00 to 1.02; p < 0.001), and a baseline mRS of 4 (OR: 2.69; 95% CI: 1.25 to 5.82; p = 0.011). The multivariable model demonstrated good predictive accuracy with an area under the receiver-operating characteristic (ROC) curve of 0.76. CONCLUSIONS: This study demonstrates that advanced age, higher NIHSS scores, elevated pre-stroke mRS, and pre-operative glucose levels significantly predict very poor outcomes in AIS-MeVO patients who received MT. These findings highlight the importance of a comprehensive risk assessment in primary MeVO patients for personalized treatment strategies. However, they also suggest a need for cautious patient selection for endovascular thrombectomy. Further prospective studies are needed to confirm these findings and explore targeted therapeutic interventions.
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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 | 0.000 | 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 | 0.000 |
| 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".