Incidental findings on cardiac computed tomography in incident hemodialysis patients: the predictors of arrhythmic and cardiovascular events in end-stage renal disease (PACE) study
Bibliographic record
Abstract
BACKGROUND: This is the first study that has examined non-cardiac incidental findings in research cardiac computed tomography (CT) of hemodialysis patients and their relationship with patient characteristics. METHODS: We performed a cross-sectional analysis in the Predictors of Arrhythmic and Cardiovascular Events in End-Stage Renal Disease (PACE) study, a prospective cohort study on incident hemodialysis patients. Non-cardiac structures in the cardiac CT scan were reviewed and evaluated. The type and frequencies of non-cardiac incidental CT findings were summarized. Univariate and multivariate logistic regression were performed to analyze the associations between gender, older age, obesity, history of cardiovascular disease (CVD), smoking status, history of chronic pulmonary disease and history of cancer with presence of any incidental CT findings and, separately, pulmonary nodules. RESULTS: Among the 260 participants, a total of 229 non-cardiac incidental findings were observed in 145 participants (55.8% of all participants). Of these findings, pulmonary nodules were the most common incidental finding (24.2% of all findings), and 41.3% of them requiring further follow-up imaging per radiology recommendation. Vascular and gastrointestinal findings occurred in 11.8% and 15.3% of participants, respectively. Participants 65 years or older had a higher odds of any incidental findings (Odds Ratio (OR) =2.55; 95% Confidence Intervals (CI) 1.30, 4.99) and pulmonary nodules (OR=4.80; 95% CI 2.51, 9.18). Prior history of CVD was independently and significantly associated with any incidental findings (OR=2.00; 95% CI 1.19, 3.40); but not with the presence of pulmonary nodules. CONCLUSIONS: We demonstrate that the prevalence of incidental findings by cardiac CT scanning is extremely high among patients on hemodialysis. Further investigations to follow-up on the high occurrence of incidental findings during our research study and potentially clinical studies raises important practical, ethical and medico-legal issues that need to be carefully considered in research projects using imaging studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".