Abstract 174: Public Health Potential of Improved Reperfusion in Thrombectomy for Stroke Based on HERMES Collaboration Data
Bibliographic record
Abstract
Introduction: The benefit that endovascular thrombectomy (EVT) offers to stroke patients with large vessel occlusions depends strongly on reperfusion grade as defined by the mTICI (modified Thrombolysis in Cerebral Infarction) scale. Our aim was to estimate the public health potential of improved reperfusion. Methods: A Markov model estimated lifetime quality-adjusted life years (QALY) of EVT-treated patients and associated costs based on mTICI grades. The analysis was performed from a United States healthcare perspective. Input parameters were based on best available evidence, including patient data from the HERMES collaboration. The lead analysis was conducted for stroke onset at 65 years. Overall lifetime costs and the net monetary benefit (NMB), which combines weighted QALYs and costs into one composite outcome, were analyzed. A willingness-to-pay threshold of $100,000 per QALY was used for NMB calculations. Results: Lifetime QALYs and the NMB per patient increased for every grade of improved mTICI reperfusion (Fig 1). The final mTICI 2C/3 rate across all trials in the HERMES collaboration was 31%, yielding on average 5.09 QALYs at lifetime costs of $230,799, resulting in a positive NMB of $278,336. Every 10% increase in the final mTICI 2C/3 rate would yield 0.16 incremental QALYs and $16,878 incremental NMB for the average patient. For a national 10% improvement of the mTICI 2C/3 rate of all EVT-treated patients in the United States, we estimated 3,645 additional QALYs and an incremental NMB of $385 million per year. Conclusions: The public health and economic impact of the grade of reperfusion is significant, warranting further improvement of devices and procedural techniques.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".