P2485Longitudinal quantitative assessment of coronary plaque progression related to glycemic status using serial coronary computed tomography angiography
Bibliographic record
Abstract
Introduction: Data on the impact of glycemic status on coronary plaque progression have been limited. This study evaluated the association between glycemic status and coronary plaque volume change using coronary computed tomography angiography (CCTA). Methods: A total of 1,296 subjects (61±9, 56.9% male) who underwent serial CCTA with available glycemic status were enrolled and analyzed from the Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography IMaging (PARADIGM) registry. The median inter-scan period was 3.2 (2.6 to 4.4) years. Quantitative assessment of coronary plaques was performed at both scans. All participants were categorized into the following groups according to glycemic status: normal, pre-diabetes (pre-DM), and DM. Results: During the follow-up period, significant differences in plaque volume change (normal: 51.3±83.3 mm3 vs. pre-DM: 51.0±84.3 mm3 vs. DM: 72.6±95.0 mm3; p<0.001) and annual plaque volume change (normal: 14.9±24.9 mm3 vs. pre-DM: 15.7±23.8 mm3 vs. DM: 21.0±27.7 mm3; p=0.001) were observed among the 3 groups. Compared with normal individuals, individuals with pre-DM showed no significant differences in the adjusted odds ratios (ORs) for plaque progression (PP) (1.338, 95% confidence interval [CI] 0.967–1.853; p=0.079). However, the adjusted OR for PP was higher in DM individuals than in normal individuals (1.635, 95% CI 1.126–2.375; p=0.010).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".