Cost-effectiveness of endovascular therapy for acute stroke with a large ischemic region in Japan: impact of the Alberta Stroke Program Early CT Score on cost-effectiveness
Bibliographic record
Abstract
BACKGROUND: Although randomized clinical trials (RCTs) demonstrated short-term benefits of endovascular therapy (EVT) for acute ischemic stroke (AIS) with a large ischemic region, little is known about the long-term cost-effectiveness or its difference by the extent of the ischemic areas. We aimed to assess the cost-effectiveness of EVT for AIS involving a large ischemic region from the perspective of Japanese health insurance payers, and analyze it using the Alberta Stroke Program Early CT Score (ASPECTS). METHODS: The Recovery by Endovascular Salvage for Cerebral Ultra-acute Embolism-Japan Large Ischemic Core Trial (RESCUE-Japan LIMIT) was a RCT enrolling AIS patients with ASPECTS of 3-5 initially determined by the treating neurologist primarily using MRI. The hypothetical cohort and treatment efficacy were derived from the RESCUE-Japan LIMIT. Costs were calculated using the national health insurance tariff. We stratified the cohort into two subgroups based on ASPECTS of ≤3 and 4-5 as determined by the imaging committee, because heterogeneity was observed in treatment efficacy. EVT was considered cost-effective if the incremental cost-effectiveness ratio (ICER) was below the willingness-to-pay of 5 000 000 Japanese yen (JPY)/quality-adjusted life year (QALY). RESULTS: EVT was cost-effective among the RESCUE-Japan LIMIT population (ICER 4 826 911 JPY/QALY). The ICER among those with ASPECTS of ≤3 and 4-5 was 19 396 253 and 561 582 JPY/QALY, respectively. CONCLUSION: EVT was cost-effective for patients with AIS involving a large ischemic region with ASPECTS of 3-5 initially determined by the treating neurologist in Japan. However, the ICER was over 5 000 000 JPY/QALY among those with an ASPECTS of ≤3 as determined by the imaging committee.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".