Evaluation of the Appropriate Use of Coronary Computed Tomography Angiography: A Retrospective, Single-Center Analysis
Bibliographic record
Abstract
Purpose: We assessed the application of appropriate use criteria of coronary computed tomography angiography (CCTA) in comparison to invasive coronary angiography results and revascularization rates in patients with coronary artery disease (CAD). Methods: 1305 patients referred to invasive coronary angiography (ICA) after CCTA were evaluated retrospectively. The primary indication for CCTA was assessed according to the consensus for intermediate-risk (15−85% pre-test probability) into appropriate (A), inappropriate (I), and uncertain while referring to published guidelines. Patients’ risk factors, angina, and heart failure symptoms (Canadian Cardiovascular Society classification (CCSC), New York Heart Association (NYHA); clinical data; and ICA results were gathered. Results: Of 1305 patients referred to CCTA prior to ICA, 496 (38.0%) were appropriate, 766 (56.9%) inappropriate, and 43 (3.3%) uncertain. Of 766 patients with inappropriate CCTA referrals, 370 (48.3%) were classified as “inappropriately low” (<15% pre-test probability) and 396 (51.7%) as “inappropriately high” (>85%) in regard to the recommended CCTA utilization. Sub-analysis of the adherence to the appropriate use criteria did not differ between the source of the referring physicians (intramural tertiary, private practice primary care, or external secondary care hospitals). Obstructive CAD with subsequent revascularization rates (total of 39.2%) did not differ significantly between the appropriate (38.3%), inappropriate (41.0%), or uncertain (23.3%) groups (p = 0.068). Conclusion: The total coronary revascularization rate after CCTA was 39.2% and not different among low, intermediate, and pre-test probability groups. These findings support the role of CCTA as an excellent gatekeeper in patients with suspected obstructive CAD even beyond pre-test probability calculation models.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".