Influence of occlusion site and baseline ischemic core on outcome in patients with ischemic stroke
Bibliographic record
Abstract
<h3>Objective</h3> We assessed patient clinical outcomes based on occlusion location, focusing on distal occlusions to understand if occlusion location was an independent predictor of outcome, and tested the relationship between occlusion location and baseline ischemic core, a known predictor of modified Rankin Scale (mRS) score at 90 days. <h3>Methods</h3> We analyzed a prospectively collected cohort of thrombolysis-eligible ischemic stroke patients from the International Stroke Perfusion Imaging Registry who underwent multimodal CT pretreatment. For the primary analysis, logistic regression was used to predict the effect of occlusion location and ischemic core on the likelihood of excellent (mRS 0–1) and favorable (mRS 0–2) 90-day outcomes. <h3>Results</h3> This study included 945 patients. The rates of excellent and favorable outcome in patients with distal occlusion (M2, M3 segment of middle cerebral artery, anterior cerebral artery, and posterior cerebral artery) were higher than M1 occlusions (mRS 0%–1%, 55% vs 37%; mRS 0%–2%, 73% vs 50%, <i>p</i> < 0.001). Vessel occlusion location was not a strong predictor of outcomes compared to baseline ischemic core (area under the curve, mRS 0–1, 0.64 vs 0.83; mRS 0–2, 0.70 vs 0.86, <i>p</i> < 0.001). There was no interaction between occlusion location and ischemic core (interaction coefficient 1.00, <i>p</i> = 0.798). <h3>Conclusions</h3> Ischemic stroke patients with a distal occlusion have higher rate of excellent and favorable outcome than patients with an M1 occlusion. The baseline ischemic core was shown to be a more powerful predictor of functional outcome than the occlusion location, but the relationship between ischemic core and outcome does not different by occlusion locations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".