Cardiac CT in asymptomatic diabetes mellitus: role of non-invasive atherosclerosis imaging in high-risk asymptomatic individuals
Bibliographic record
Abstract
The negative prognostic importance of diabetes mellitus (DM) on cardiovascular outcomes is well known. DM is considered as coronary artery disease (CAD) equivalent, which places an asymptomatic individual with DM in the high-risk group. Direct coronary imaging of CAD in asymptomatic high-risk individuals is controversial. In particular, the role of non-invasive cardiac computed tomography (CCT) in asymptomatic diabetics is less well understood.1–6 In this issue of the Journal, Kim et al.6 shed light on this topic with the results of the CRONOS-ADM prospective registry (CoROnary CT aNgiography evaluation for clinical OutcomeS in Asymptomatic patients with type 2 Diabetes Mellitus). These data provide insight into several aspects of the DM relationship with time and CAD extent using CCT in an asymptomatic population of individuals. The relevant findings of the study are that DM duration is associated with progression in the extent and severity of CAD which independently predicts an increased risk of major adverse cardiac events (MACE) beyond traditional risk factors. Therefore, as already demonstrated in general population of patients with suspected CAD assessed with CCT, CAD burden seems to be a strong predictor of events. In addition, the prevalence of normal coronary arteries on CCT was progressively reduced with increased DM duration (from 31% with DM duration <5 years to 16% with DM duration >10 years).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.007 | 0.007 |
| 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".