Predictors of 90-Day Functional Outcome Following Direct Mechanical Thrombectomy for Anterior Circulation Large Vessel Occlusion: A Prospective Study
Bibliographic record
Abstract
Background: Mechanical thrombectomy (MT) is becoming a growing trend in the management of large vessel occlusion (LVO) during the past few decades, although data on the predictors of outcome following MT are scarce. We aimed to study the predictors of 90-day outcome in a cohort of patients with ischemic stroke with large-vessel occlusion. Methods: This was a three-month prospective study of 40 patients with anterior circulation LVO who underwent MT and were followed up for three months with modified Rankin Score (mRS). Results: Of the 40 patients recruited, 55% were men. M1 was the most common vessel occluded (32.5%) followed by internal carotid artery (ICA) and carotid trunk (20%). Tandem occlusion occurred in 25% of the cases. Among the demographic, clinical, radiological, and procedural variables studied, the factors that had a significant impact on the mRS at 3 months were age, diabetes mellitus (DM), hyperlipidemia, stroke mechanism, blood glucose level during procedure, post-procedural National Institutes of Health Stroke Scale (NIHSS), baseline Alberta stroke program early CT score (ASPECT) score, collaterals grade, and procedural thrombolysis in cerebral infarction (TICI) score (P<0.05). On multivariate regression, patients’ age (B: 0.025, 95% CI: 0.001- 0.049, P=0.038), post-procedural NIHSS (B: 0.192, 95% CI: 0.101–0.283, P<0.001), and baseline ASPECT score (B: -0.442, 95% CI: -0.838- -0.046, P=0.03) were the most independent factors to affect the mRS at 3 months. Conclusion: Patients’ age, baseline ASPECT score and post-procedural NIHSS are significant predictors of 90-day outcome of large-vessel occlusion following MT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".