Cost-effectiveness analysis in patients with an unruptured cerebral aneurysm treated with observation or surgery
Bibliographic record
Abstract
OBJECTIVE: The main goal of preventive treatment of unruptured intracranial aneurysms (UIAs) is to avoid the morbidity and mortality associated with aneurysmal subarachnoid hemorrhage. A comparison between the conservative approach and the surgical approach combining endovascular treatment and microsurgical clipping is currently lacking. This study aimed to conduct an updated evaluation of cost-effectiveness comparing the two approaches in patients with UIA. METHODS: A decision tree with a Markov model was developed. Quality-adjusted life-years (QALYs) associated with living with UIA before and after treatment were prospectively collected from a cohort of patients with UIA at a tertiary center. Other inputs were obtained from published literature. Using Monte Carlo simulation for patients aged 55, 65, and 75 years, the authors modeled the conservative management in comparison with preventive treatment. Different proportions of endovascular and microsurgical treatment were modeled to reflect existing practice variations between treatment centers. Outcomes were assessed in terms of QALYs. Sensitivity analyses to assess the model's robustness and completed threshold analyses to examine the influence of input parameters were performed. RESULTS: Preventive treatment of UIAs consistently led to higher utility. Models using a higher proportion of endovascular therapy were more cost-effective. Models with older cohorts were less cost-effective than those with younger cohorts. Treatment was cost-effective (willingness to pay < 100,000 USD/QALY) if the annual rupture risk exceeded a threshold between 0.8% and 1.9% in various models based on the proportion of endovascular treatment and cohort age. A higher proportion of endovascular treatments and younger age lowered this threshold, making the treatment of aneurysms with a lower risk of rupture more cost-effective. CONCLUSIONS: Preventive treatment of aneurysms led to higher utility compared with conservative management. Models with a higher proportion of endovascular treatment and younger patient age were most cost-effective.
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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 | 0.004 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".