Abstract 4221: Cardiac Computed Tomography Angiography in the Setting of Low-Moderate Coronary Artery Disease Risk - Do Results Change Management?
Bibliographic record
Abstract
CCTA is sensitive and specific for detecting CAD, but the impact on patient management and outcomes is unknown. We describe our experience with consecutive cases performed at a single CCTA centre. Coronary calcium scoring and contrast-enhanced coronary CT angiograms were performed on 114 patients using a Toshiba 64-slice scanner and standard protocols. Information on demographics, risk factors, symptomatology, prior cardiac investigations and medications was collected by questionnaire and verified by the consulting cardiologist just prior to scan performance. Scan results and management recommendations were recorded. Participants were primarily male (76.1%) with a mean age of 59.8±9.9yrs. CAD risk factor prevalence was: 53.2% hypertensive, 10.6% diabetic, 49% ever smoked, 75.7% with a family history of premature CAD, and mean LDL and HDL were 2.68±0.81 and 1.4±0.43 μmol/L. Baseline medical therapy is listed in Table I . The prevalence of chest discomfort symptoms was: none (55.4%), non-cardiac (4.3%), atypical (15.2%) and typical (25.1%). Prior investigations included MPI (42.1%), and EST (53.5%) with results that were negative (50%, 45.9%) positive (35.4%, 23%) or equivocal (14.6%, 27.9% respectively). A pre-existing diagnosis of CAD was present in 21.9%. Median coronary calcium (Agatston) score was 79 ( range 0→1899). CAD was diagnosed in 71% of patients, and luminal stenosis was >50% in 70% (n=54). Management recommendations included changes to medical therapy (Table I ), further testing (EST (n=3), MPI (n=6), angiography (n=14)), and test cancellation (MPI (n=2)). In no patients did CCTA result in the cancellation of cardiac catheterization. CCTA provided a new diagnosis of CAD in 49% of patients of low-moderate CAD risk, and resulted in a net increase in the number of medications and investigations ordered. The impact on patient morbidity and mortality is the subject of further study. Table I: Medications pre and post CCTA results
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".