Baseline predictors of poor clinical outcome despite recanalization of distal middle cerebral artery occlusions
Bibliographic record
Abstract
Objective Mechanical thrombectomy (MT) is well-established for the treatment of acute ischemic stroke (AIS) from large vessel occlusion (LVO), with growing data supporting the expansion to distal and medium vessel occlusions (DMVO). Despite successful recanalization in DMVO, certain patients still experience poor long-term clinical outcomes, prompting our study to comprehensively explore pre-MT factors influencing outcome despite excellent recanalization (final modified Thrombolysis in Cerebral Infarction [mTICI] score ≥2c). Methods We retrospectively examined data from patients who consecutively underwent MT for a primary middle cerebral artery (MCA) DMVO across 37 centers in North America, Asia, and Europe. We identified baseline clinical and imaging factors associated with poor clinical outcome (defined as a modified Rankin Scale [mRS] score of 3–6) at 3 months, despite excellent recanalization using a multivariable model. Results Between September 2017 and July 2021, 623 patients achieved mTICI > 2b and they were included in our study. Among them, 198 (32%) experienced a poor clinical outcome (mRS 3–6). Predictors of poor clinical outcome included higher age (OR 1.05 [1.03–1.07], p < 0.001), higher NIHSS at admission (OR 1.12 [1.08–1.15], p < 0.001), higher baseline mRS (OR 1.77 [0.96–3.26], p = 0.067), and diabetes (OR 1.59 [1.01–2.48], p = 0.044). Higher ASPECTS was associated with a decreased risk of poor clinical outcome (OR 0.82 [0.71–0.94], p = 0.006). Conclusion Older age, diabetes, higher baseline mRS, and NIHSS were associated with poor clinical outcome in MCA DMVO despite excellent recanalization. Conversely, a higher ASPECTS decreased the probability of such an outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".