Predictors of three months mortality after endovascular mechanical thrombectomy for acute ischemic stroke
Bibliographic record
Abstract
Abstract Objective This study aims to determine predictive factors of mortality within 3 months after mechanical thrombectomy (MT) for patients with acute ischemic stroke (AIS). Methods This prospective cohort study was carried out at Assiut University stroke center in collaboration with Ain Shams University Egypt. Forty-four patients with anterior circulation AIS underwent MT between the first of January 2020 to the end of December 2020. They were evaluated for detection of death rates and identification of risks of 3-month mortality after MT. Results This study comprised 44 patients (52.3% male) with a mean age of 64.3 years. The mean time from onset to femoral arterial puncture was 304.1 min. The mean Alberta Stroke Program Early CT Score (ASPECTS) and National Institutes of Health Stroke Scale (NIHSS) score were 9.3 and 19.2, respectively. The 3-month mortality rate was 13.6%. Univariate analyses found that higher baseline NIHSS score (mean 22.2 vs. 18.7, p = 0.021), absence of good collateral (16.7% vs. 68.4%, p = 0.02), longer duration of procedure ≥ 60 min (66.7% vs. 23.7%, p = 0.032), lower rates of successful recanalization (83.3% vs. 28.9%, p = 0.01) and developing symptomatic intracerebral hemorrhage (33.3% vs. 5.3%, p = 0.026) were associated with 3-month mortality. Multivariate analyses found that low rates of successful recanalization (OR 0.081; 95% CI 0.009–0.780, p = 0.030) remained significant independent predictive factor of 3-month mortality. Conclusion Successful recanalization was an independent predictor of 3-month mortality after 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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".