Comparison of cardiac computed tomography recommendations in recent ESC vs. ACC/AHA guidelines
Bibliographic record
Abstract
Cardiac computed tomography (CCT) continues to expand with increasing applications and technological advancements. Growing evidence on the clinical utility of CCT necessitates evaluating how this knowledge is incorporated into European Society of Cardiology (ESC) and American College of Cardiology (ACC)/American Heart Association (AHA) guidelines. We aimed to provide a comprehensive comparison of CCT indications between ESC and ACC/AHA guidelines to identify areas of consensus and divergence in the current landscape of CCT utilization. ESC and ACC/AHA guidelines were systematically reviewed for CCT recommendations. The class of recommendation (COR) and level of evidence (LOE) were compared using χ2 or Fisher exact tests. The latest ESC guidelines included 40 recommendations regarding CCT: 18 (45%) COR-I, 14 (35%) COR-IIa, 6 (15%) COR-IIb, and 2 (5%) COR-III. Two (5%) recommendation had LOE-A, 20 (50%) had LOE-B, and 18 (45%) had LOE-C. The latest ACC/AHA guidelines consisted of 54 recommendations: 18 (33.3%) COR-I, 28 (51.9%) COR-IIa, 6 (11.1%) COR-IIb, and 2 (3.7%) COR-III. Two recommendations were assigned LOE-A (3.7%), 30 (55.6%) were classified as LOE-B, and 22 (40.7%) as LOE-C. ACC/AHA guidelines had a significantly higher proportion of COR-IIa recommendations (P = 0.04) and similar proportions of COR-I and COR-IIb recommendations (P = 0.28; P = 0.76), compared to ESC guidelines. The proportion of LOE-B and LOE-C recommendations weren't statistically different (P = 0.54; P = 0.84). ACC/AHA guidelines included more CCT recommendations with a higher COR and LOE than ESC guidelines. These findings highlight the need for continued research and consensus-building to establish standardized, evidence-based CCT recommendations in clinical practice.
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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 | 0.051 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".