Factors affecting mechanical thrombectomy outcome in acute ischemic stroke patients: an Egyptian sample
Bibliographic record
Abstract
Abstract Background Stroke constitutes a major health problem worldwide, nowadays endovascular treatment is considered to be a standard of care in acute ischemic stroke patients with large vessel occlusion, accurate prediction of outcome of thrombectomy is essential for health care providers, patients and families. We studied different clinical and radiological variables that could predict functional outcome in stroke patients after thrombectomy. Thirty-four consecutive ischemic stroke patients were included, received intravenous tissue plasminogen activator (iv-tpa), then mechanical thrombectomy was done. Patients were clinically assessed at admission with National Institute of Health Stroke Scale (NIHSS) then evaluated by the Alberta Stroke Program early computerized tomography (CT) score (ASPECTS), clot burden scale (CBS) and collateral score (CS) in multiphase computerized tomography angiography (CTA) then good and poor outcomes at 3 months were defined by Modified Rankin Scale (MRS) of 0–2 and 3–6 points, respectively. Results Factors associated with good outcome (MRS 0–2) were lower admission NIHSS score (p < 0.037), small infarct core aspects ≥ 6 (p < 0.001), low clot burden (CBS 7–10) (p = 0.046) good collaterals (2–3) (p = 0.038) and absence of post-procedure hemorrhage (p < 0.0005). Conclusions Low admission NIHSS score, absence of post-procedure hemorrhage, small infarct core, low clot burden and good collaterals are reliable factors for good clinical outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".