Cost-effectiveness of magnetic resonance carotid plaque imaging for primary stroke prevention in Canada
Bibliographic record
Abstract
OBJECTIVE: Magnetic resonance of the carotid arteries provides important insight into plaque composition and vulnerability in addition to the traditional measure of stenosis. The purpose of this study was to evaluate the cost-effectiveness of MR imaging as a first-line modality to assess carotid disease and guide management for high-risk patients with <50% stenosis. METHODS: Using TreeAge Pro, a cost-effectiveness simulation was conducted comparing two strategies: (a) standard of care first-line carotid duplex ultrasound (DUS) with regular follow-up, vs (b) first-line MR assessment of stenosis and intraplaque haemorrhage (MRIPH) in which patients with IPH received annual DUS surveillance and immediate carotid endarterectomy in case of plaque progression. RESULTS: For patients aged 70 years old, using a first-line MRIPH strategy resulted in a 16.8% relative risk reduction in strokes compared to DUS (0.080 vs 0.097 strokes per patient per lifetime), and an increased quality-adjusted-life years (12.23 vs 12.20) at an increased cost of $897.33 over a patient's lifetime ($5784.53 vs $4887.20 average total cost per patient per lifetime). The incremental cost-effectiveness ratio was $29,744 per quality-adjusted-life years. MRIPH remained cost-effective below a willingness-to-pay threshold of $50,000 for 91.8% of sensitivity analyses. CONCLUSION: MRIPH was found to be a cost-effective first-line tool to identify asymptomatic patients at high risk for stroke requiring annual surveillance and prompt management. Advances in Knowledge: Using MR imaging as a fist-line method to detect the presence of IPH provides clinically useful and cost-effective information that allows for enhanced risk evaluation and primary stroke prevention.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".