Appropriate Use Criteria for Cardiac Computed Tomography
Bibliographic record
Abstract
PURPOSE: Cardiac imaging expenditures have come under scrutiny, and a focus on appropriate use criteria (AUC) has arisen to ensure cost-effective resource utilization. Although AUC has been developed by clinical experts, it has not undergone rigorous quality assurance testing to ensure that inappropriate indications for testing yield little clinical benefit. The objective of the study was to evaluate the potential incremental prognostic value of coronary computed tomographic angiography (CCTA) in the different AUC categories. MATERIALS AND METHODS: Consecutive patients enrolled into a cardiac CT Registry were collated. Patient indications were reviewed and based on the 2010 AUC (appropriate, uncertain, and inappropriate). Patients were followed-up for death, myocardial infarction (MI), and late revascularization, with the primary composite endpoint being cardiac death, nonfatal MI, and late revascularization. The prognostic value of CCTA over clinical variables in each of the AUC categories was assessed. RESULTS: Indications for CCTA were appropriate, uncertain, and inappropriate in 1284 (66.5%), 312 (16.2%), and 334 (17.3%) patients, respectively. Rates of all-cause of death, cardiac death, nonfatal MI, and late revascularization were similar across patients with appropriate, uncertain, and inappropriate indications for CCTA. Moreover, in each AUC category, CCTA had incremental prognostic value over a routine clinical risk score (National Cholesterol Education Program) with hazard ratios of 9.98, 7.39, and 5.61. CONCLUSIONS: CCTA has incremental prognostic value in all AUC categories, even when the reason for the study was deemed "inappropriate." This suggests that CCTA may still have clinical value in "inappropriate" indications and that further quality assurance AUC studies are needed.
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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 | 0.011 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".