Medical management versus intravenous thrombolysis for patients with minor non-disabling acute ischemic stroke: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: The efficacy and safety of thrombolysis therapy in patients with mild stroke, especially acute non-disabling stroke is controversial. We intend to conduct this systematic review and meta- analysis to evaluate the efficacy and safety of thrombolytic therapy compared to medical management in acute non-disabling stroke. Methods: We searched multiple databases to obtain articles related to medical management and intravenous thrombolysis therapy for minor non-disabling acute ischemic stroke from inception until November 28, 2023, and the search was conducted again on September 1, 2024. The primary outcome was functional independence (modified Rankin scale [mRS] score of 0 to 2) at 90 days. All analyses were performed using the random effect model. The quality of articles was evaluated through the Cochrane risk assessment tool and Newcastle-Ottawa scale. Results: 2 RCTs and 7 cohort studies met the inclusion criteria. The merge analysis showed that there was no significant difference in improving functional independence (mRS 0-2, RR: 1.01, 95% CI 0.98 - 1.04, P = 0.47) and excellent outcome (mRS 0-1) of patients with minor non-disabling acute ischemic stroke between IVT and medical management. However, IVT would increase the risk of early neurological deterioration (RR: 0.50, 95% CI 0.30 - 0.82, P = 0.007), compared to medical management. Analysis of the cohort studies showed that there was a significant correlation between IVT and sICH (RR: 0.20, 95% CI: 0.06 - 0.68, P=0.01). Conclusions: For patients with minor non-disabling acute ischemic stroke, medical management will not have a negative impact on functional recovery, and may be a safer alternative.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.041 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".