The feasibility of mechanical thrombectomy versus medical management for acute stroke with a large ischemic territory
Bibliographic record
Abstract
BACKGROUND: Mechanical thrombectomy (MT) for acute ischemic stroke is generally avoided when the expected infarction is large (defined as an Alberta Stroke Program Early CT Score of <6). OBJECTIVE: To perform a meta-analysis of recent trials comparing MT with best medical management (BMM) for treatment of acute ischemic stroke with large infarction territory, and then to determine the cost-effectiveness associated with those treatments. METHODS: A meta-analysis of the RESCUE-Japan, SELECT2, and ANGEL-ASPECT trials was conducted using R Studio. Statistical analysis employed the weighted average normal method for calculating mean differences from medians in continuous variables and the risk ratio for categorical variables. TreeAge software was used to construct a cost-effectiveness analysis model comparing MT with BMM in the treatment of ischemic stroke with large infarction territory. RESULTS: The meta-analysis showed significantly better functional outcomes, with higher rates of patients achieving a modified Rankin Scale score of 0-3 at 90 days with MT as compared with BMM. In the base-case analysis using a lifetime horizon, MT led to a greater gain in quality-adjusted life-years (QALYs) of 3.46 at a lower cost of US$339 202 in comparison with BMM, which led to the gain of 2.41 QALYs at a cost of US$361 896. The incremental cost-effectiveness ratio was US$-21 660, indicating that MT was the dominant treatment at a willingness-to-pay of US$70 000. CONCLUSIONS: This study shows that, besides having a better functional outcome at 90-days' follow-up, MT was more cost-effective than BMM, when accounting for healthcare cost associated with treatment outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.017 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".