Endovascular thrombectomy can be beneficial to acute ischemic stroke patients with large infarcts
Bibliographic record
Abstract
OBJECTIVE: This study aimed to assess whether patients with acute ischemic stroke (AIS) and large infarct lesions benefit from reperfusion management. To determine the efficacy of different recanalization managements on AIS patients with Alberta Stroke Program Early CT Score (ASPECTS) < 6, the authors retrospectively analyzed hospitalized patients with AIS. METHODS: Eighty-nine patients with AIS and ASPECTS < 6 were screened from 13,285 hospitalized patients treated by thrombolysis, thrombectomy, or conventional care in two stroke medical centers. Logistic regression or Fisher's exact test was performed for comparison of the outcome and risk events between patients treated by thrombectomy (or thrombolysis) and conventional care. The modified Rankin Scale (mRS) score was used to assess the major clinical outcome of patients 3 months after disease onset. Disease outcome was also examined by analyzing symptom improvement at discharge. In particular, mortality and symptomatic intracranial hemorrhage (sICH) were evaluated as risk factors. RESULTS: This study included 21 patients who received thrombolysis, 36 patients receiving thrombectomy, and 32 patients receiving conventional treatment. Among these 3 treatments, only the thrombectomy group clearly showed the most encouraging clinical outcome (mRS score 0-2; p < 0.05, Fisher's exact test) and marked improvement (OR 25.84, 95% CI 2.44-273.59) compared with conventional treatment. It is noteworthy that the mortality rate of the thrombectomy and thrombolysis group was similar to that of the conventional group, and thrombectomy and thrombolysis increased the risk of sICH in comparison with conventional care (p < 0.05, Fisher's exact test). CONCLUSIONS: Patients with AIS and ASPECTS < 6 definitely benefited from thrombectomy with higher sICH risk, whereas thrombolysis management showed similar efficacy to the control group.
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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 | 0.000 | 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".