Appropriate Use Criteria for Cardiac Computed Tomography: Impact on Diagnostic Utility
Bibliographic record
Abstract
BACKGROUND: Appropriate Use Criteria (AUC) guidelines for cardiac computed tomography (CCT) were developed to limit testing to reasonable clinical settings. However, significant testing is still done for inappropriate indications. This study investigates the impact of AUC on evaluability of CCT to determine if inappropriate tests result in a greater proportion of nondiagnostic results. METHODS: Investigators reviewed the medical records of 2417 consecutive patients who underwent CCT at the University of Ottawa Heart Institute. We applied the 2010 AUC and classified them as appropriate, inappropriate, or uncertain. Unclassifiable tests, as well as those with uncertain appropriateness, were excluded from the final analysis. Cardiac computed tomography results were classified as diagnostic if (1) all coronary segments were visualized, evaluable, and without obstructive stenosis; or (2) obstructive coronary artery disease with greater than 50% diameter stenosis in at least 1 coronary artery. All other test results were considered nondiagnostic. RESULTS: Of the 1984 patients included in the final analysis, 1522 patients (76.7%) had indications that were appropriate, whereas the remaining 462 (23.3%) were inappropriate. Inappropriate tests resulted in a higher rate of nondiagnostic results compared with appropriate CCT (9.0% vs 6.2%, P = 0.034). Inappropriate tests also had significantly more studies with nonevaluable segments than appropriate tests (24.5% vs 16.4%, P < 0.001) and were more likely to reveal obstructive coronary disease than appropriate CCT (50.5% vs 32.7%, P < 0.001). CONCLUSIONS: Cardiac computed tomography done for inappropriate indications may be associated with lower diagnostic yield and could impact future downstream resource utilization and health care costs.
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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 | 0.061 | 0.429 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".