Mechanical thrombectomy is cost-effective versus medical management alone around Europe in patients with low ASPECTS
Bibliographic record
Abstract
OBJECTIVE: To demonstrate, by a cost-effectiveness analysis, the efficiency of mechanical thrombectomy (MT) versus medical management (MM) in patients with a low Alberta Stroke Program Early CT Score (ASPECTS) from the RESCUE Study. METHODS: A cost-effectiveness model was designed to project both direct medical costs and quality-adjusted life-years (QALYs) of MT versus MM in eight European countries (Spain, UK, France, Italy, Belgium, Germany, Sweden, and the Netherlands). Our model was created based on previously published health-economic data in those countries. Procedure costs, acute, mid-term, and long-term care costs were projected based on expected modified Rankin Scale (mRS) scores as reported in the RESCUE-Japan LIMIT trial. RESULTS: MT was found to be a cost-effective option in eight different countries across Europe (Spain, Italy, UK, France, Belgium, Germany, the Netherlands, and Sweden). with a lifetime incremental cost-effectiveness ratio varying from US$2 875 to US$11 202/QALY depending on the country. A cost-effectiveness acceptability curve showed 100% acceptability of MT at the willingness to pay (WTP) of US$40 000 for the eight countries. CONCLUSIONS: MT is efficient versus MM alone for patients with low ASPECTS in eight countries across Europe. Patients with a large ischemic core could be treated with MT because it is both clinically beneficial and economically sustainable.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".