The Cost-Utility of CT Angiography and Conventional Angiography for People Presenting with Intracerebral Hemorrhage
Bibliographic record
Abstract
OBJECTIVE: To determine the optimal imaging strategy for ICH incorporating CTA or DSA with and without a NCCT risk stratification algorithm. METHODS: A Markov model included costs, outcomes, prevalence of a vascular lesion, and the sensitivity and specificity of a risk stratification algorithm from the literature. The four imaging strategies were: (a) CTA screening of the entire cohort; (b) CTA only in those where NCCT suggested a high or indeterminate likelihood of a lesion; (c) DSA screening of the entire cohort and (d) DSA only for those with a high or indeterminate suspicion of a lesion following NCCT. Branch d was the comparator. RESULTS: Age of the cohort and the probability of an underlying lesion influenced the choice of optimal imaging strategy. With a low suspicion for a lesion (<12%), branch (a) was the optimal strategy for a willingness-to-pay of $100,000/QALY. Branch (a) remained the optimal strategy in younger people (<35 years) with a risk below 15%. If the probability of a lesion was >15%, branch (b) became preferred strategy. The probabilistic sensitivity analysis showed that branch (b) was the optimal choice 70-72% of the time over varying willingness-to-pay values. CONCLUSIONS: CTA has a clear role in the evaluation of people presenting with ICH, though the choice of CTA everyone or CTA using risk stratification depends on age and likelihood of finding a lesion.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".