Cost-effectiveness of endovascular treatment versus best medical management in basilar artery occlusion stroke: A U.S. healthcare perspective
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Two recent studies showed clinical benefit for endovascular treatment (EVT) in basilar artery occlusion (BAO) stroke up to 12 h (ATTENTION) and between 6 and 24 h from onset (BAOCHE). Our aim was to investigate the cost-effectiveness of EVT from a U.S. healthcare perspective. MATERIALS AND METHODS: Clinical input data were available for both trials, which were analyzed separately. A decision model was built consisting of a short-run model to analyze costs and functional outcomes within 90 days after the index stroke and a long-run Markov state transition model (cycle length of 12 months) to estimate expected lifetime costs and outcomes from a healthcare and a societal perspective. Incremental cost-effectiveness ratios (ICER) were calculated, deterministic (DSA) and probabilistic (PSA) sensitivity analyses were performed. RESULTS: EVT in addition to best medical management (BMM) resulted in additional lifetime costs of $32,063 in the ATTENTION trial and lifetime cost savings of $7690 in the BAOCHE trial (societal perspective). From a healthcare perspective, EVT led to incremental costs and effectiveness of $37,389 and 2.0 QALYs (ATTENTION) as well as $3516 and 1.9 QALYs (BAOCHE), compared to BMM alone. The ICER values were $-4052/QALY (BAOCHE) and $15,867/QALY (ATTENTION) from a societal perspective. In each trial, PSA showed EVT to be cost-effective in most calculations (99.9%) for a willingness-to-pay threshold of $100,000/QALY. Cost of EVT and age at stroke represented the greatest impact on the ICER. DISCUSSION: From an economic standpoint with a lifetime horizon, EVT in addition to BMM is estimated to be highly effective and cost-effective in BAO stroke.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it