Cost-effectiveness of targeted thrombolytic therapy for stroke patients using multi-modal CT compared to usual practice
Bibliographic record
Abstract
INTRODUCTION: The use of multimodal computed tomography imaging (MMCT) in routine clinical assessment of stroke patients improves the identification of patients with large regions of salvageable brain tissue, lower risk for haemorrhagic transformation, or a large vessel occlusion requiring endovascular therapy. AIM: To evaluate the cost-effectiveness of using MMCT compared to usual practice for determining eligibility for reperfusion therapy with alteplase using real world data from the International Stroke Perfusion Imaging Registry (INSPIRE). METHODS: We performed a cost-utility analysis. Mean costs and quality-adjusted life years (QALYs) per patient for two alternative screening protocols were calculated. Protocol 1 represented usual practice, while Protocol 2 reflected treatment targeting using multimodal imaging. Cost-effectiveness was assessed using the net-benefit framework. RESULTS: Protocol 1 had a total mean per patient cost of $2,013 USD and 0.148 QALYs. Protocol 2 had a total mean per patient cost of $1,519 USD and 0.153 QALYs. For a range of willingness-to-pay values, representing implicit thresholds of cost-effectiveness, the lower bound of the incremental net monetary benefit statistic was consistently greater than zero, indicating that MMCT is cost- effective compared to usual practice. The results were most sensitive to variation in the mean number of alteplase vials administered. CONCLUSION: In a healthcare setting where multimodal imaging technologies are available and reimbursed, their use in screening patients presenting with acute stroke to determine eligibility for alteplase treatment is cost-effective given a range of willingness-to-pay thresholds and warrants consideration as an alternative to routine practice.
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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 | 0.005 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".