Extracardiac Findings in Cardiac Computed Tomographic Angiography in Patients at Low to Intermediate Risk for Coronary Artery Disease
Bibliographic record
Abstract
PURPOSE: To evaluate the prevalence, clinical significance, interobserver agreement, and follow-up of extracardiac findings on coronary computed tomographic angiography (CTA). METHODS: A prospectively recruited cohort of 80 patients at low to intermediate risk of having coronary artery disease underwent CTA with field of view imaging from lung apices to upper abdomen. Two staff radiologists read each scan independently. Scans read by reader no. 1 were read as part of routine clinical practice, and the findings were subsequently reclassified to potentially significant, as defined by requiring clinical or radiologic follow-up, and insignificant by a separate observer, whereas reader no. 2 retrospectively read and autonomously classified the findings as potentially significant or insignificant. RESULTS: Reader no. 1 found 7 potentially significant findings in 7 patients and 33 insignificant findings in 29 patients. Reader no. 2 found 10 potentially significant findings in 10 patients and 59 insignificant findings in 42 patients. Inter-rater agreement was moderate (kappa = 0.49; 95% confidence interval, 0.31-0.67) for the presence vs the absence of extracardiac findings and moderate (kappa = 0.52; 95% confidence interval, 0.15-0.89) for the presence of potentially significant extracardiac findings. The most common potentially significant finding was possibly malignant lung nodule (n = 6 [reader 1], 4 [reader 2]). Four patients with potentially significant findings received follow-up imaging, and 1 patient underwent biopsy, which was complicated by pneumothorax. No diagnoses of malignancy were made. CONCLUSIONS: Extracardiac findings are frequent and moderately reproducible, however, in this study, not associated with clinical benefit. Large prospective studies are required to establish whether reporting of extracardiac findings is associated with improved patient outcomes.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".