Baseline clinical and neuroradiological predictors of outcome in patients with large ischemic core undergoing mechanical thrombectomy: A retrospective multicenter study
Bibliographic record
Abstract
BACKGROUND: Recent randomized trials have shown the benefit of mechanical thrombectomy (MT) also in patients with an established large ischemic core. AIMS: The purpose of this study was to define baseline predictors of clinical outcome in patients with large vessel occlusion (LVO) in the anterior circulation and an Alberta Stroke Program Early CT score (ASPECTS) ⩽ 5, undergoing MT. MATERIAL AND METHODS: The databases of 16 comprehensive stroke centers were retrospectively screened for patients with LVO and ASPECTS ⩽5 that received MT. Baseline clinical and neuroradiological features, including the differential contribution of all ASPECTS regions to the composite score, were collected. Primary clinical outcome measure was a 90-day modified Rankin Scale (mRS) score of 0-2. Statistical analysis used a logistic regression model and random forest algorithm. RESULTS: A total of 408 patients were available for analysis. In multivariate model, among baseline features, lower age (odd ratio (OR) = 0.962, 95% confidence interval (CI) = 0.943-0.982) and lower National Institute of Health Stroke Scale (NIHSS) score (OR = 0.911, 95% CI = 0.862-0.963) were associated with the mRS score 0-2. Involvement of the M2 (OR = 0.398, 95% CI = 0.206-0.770) or M4 (OR = 0.496, 95% CI = 0.260-0.945) ASPECTS regions was associated with an unfavorable outcome. Random forest analysis confirmed that age and baseline NIHSS score are the most important variables influencing clinical outcome, whereas involvement of cortical regions M5, M4, M2, and M1 can have a negative impact. CONCLUSION: Our retrospective analysis shows that, along with age and baseline clinical impairment, presence of early ischemic changes involving cortical areas has a role in clinical outcome in patients with large ischemic core undergoing MT. DATA ACCESS STATEMENT: The data that support the findings of this study are available upon reasonable request.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".