P.048 Cost-effectiveness of tenecteplase compared to alteplase for acute ischemic stroke from a Canadian perspective
Bibliographic record
Abstract
Background: Tenecteplase is a genetically-modified variant of the tissue plasminogen activator alteplase, with increased fibrin-specificity, administered as a more convenient intravenous bolus. Recent data, from the AcT trial, have shown tenecteplase to be non-inferior to alteplase in patients with acute ischemic stroke (AIS) treated within 4.5 hours from symptom onset, the direction of effect favoring tenecteplase. As a result, the Heart and Stroke Foundation of Canada has added tenecteplase to the Stroke Best Practice Recommendations. However, its cost-effectiveness in the Canadian setting remains unknown. Methods: An analysis was performed to estimate the cost-effectiveness of tenecteplase compared to alteplase in the AIS population. The model structure combines a decision tree for the first 90 days post index stroke, where the 7 modified Rankin Scale (mRS) states are informed by the AcT trial, and a Markov model for the remainder of the lifetime horizon. Cost and utility values were derived from the literature and public sources. Canadian health care system and hospital perspectives were used. Results: This economic analysis demonstrates that tenecteplase is dominant compared to alteplase, providing more quality-adjusted life years at lower costs. Conclusions: Adding tenecteplase to hospital formularies for AIS would generate savings for the health care system while providing more benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".