Cost-Effectiveness of Late Endovascular Thrombectomy vs. Best Medical Management in a Clinical Trial Setting and Real-World Setting
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To assess cost-effectiveness of late time-window endovascular treatment (EVT) in a clinical trial setting and a "real-world" setting. METHODS: Data are from the randomized ESCAPE trial and a prospective cohort study (ESCAPE-LATE). Anterior circulation large vessel occlusion patients presenting > 6 hours from last-known-well were included, whereby collateral status was an inclusion criterion for ESCAPE but not ESCAPE-LATE. A Markov state transition model was built to estimate lifetime costs and quality-adjusted life-years (QALYs) for EVT in addition to best medical care vs. best medical care only in a clinical trial setting (comparing ESCAPE-EVT to ESCAPE control arm patients) and a "real-world" setting (comparing ESCAPE-LATE to ESCAPE control arm patients). We performed an unadjusted analysis, using 90-day modified Rankin Scale(mRS) scores as model input and analysis adjusted for baseline factors. Acceptability of EVT was calculated using upper/lower willingness-to-pay thresholds of 100,000 USD/50,000 USD/QALY. RESULTS: = 20). Late EVT in addition to best medical care was cost effective in the unadjusted analysis both in the clinical trial and real-world setting, with acceptability 96.6%-99.0%. After adjusting for differences in baseline variables between the groups, late EVT was marginally cost effective in the clinical trial setting (acceptability:49.9%-61.6%), but not the "real-world" setting (acceptability:32.9%-42.6%). CONCLUSION: EVT for LVO-patients presenting beyond 6 hours was cost effective in the clinical trial setting and "real-world" setting, although this was largely related to baseline patient differences favoring the "real-world" EVT group. After adjusting for these, EVT benefit was reduced in the trial setting, and absent in the real-world setting.
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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 | 0.029 | 0.053 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".