2615 Decision-analytic modelling of strategies for investigating suspected acute aortic syndrome
Bibliographic record
Abstract
Aims and Objectives Acute aortic syndrome (AAS) requires urgent diagnosis with computer tomographic angiography (CTA). Diagnostic strategies using clinical scores and blood tests can select patients for CTA. Identifying an appropriate strategy involves weighing the benefits of detecting AAS against the harms and costs of over-investigation. We aimed to estimate the cost-effectiveness of diagnostic strategies using the Aortic Dissection Detection Risk Score (ADD-RS) and/or D-dimer to select patients with potential symptoms of AAS for CTA. Method and Design We developed a decision-analytic model to simulate the management of patients attending hospital with possible AAS. We modelled diagnostic strategies that used the ADD-RS and/or D-dimer to select patients for CTA. We used estimates from our meta-analysis, existing literature, and clinical experts to model the consequences of diagnostic strategies upon survival, health utility, and health and social care costs. We estimated the incremental cost per quality-adjusted life years (QALYs) gained by each strategy compared to the next most effective alternative on the efficiency frontier. Results and Conclusion A strategy based on the Canadian guideline (CTA if ADD-RS>1 or ADD-RS=1 with D-dimer>500ng/mL) is cost-effective but would result in high rates of CTA if applied to an unselected population (AAS prevalence 0.26%). The strategy is also cost-effective and would result in lower rates of CTA if applied to a more selected population, such as those with a non-zero clinical suspicion of AAS (prevalence 0.61%). For patients currently receiving CTA, using ADD RS>1 or D-Dimer>500ng/mL to select patients for CTA is cost-effective. A strategy using ADD RS>1 or ADD RS=1 with D-dimer>500ng/mL to select patients for CTA appears cost-effective but primary research is required to evaluate this strategy in practice and determine how suspicion of AAS is identified.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".