Coronary artery calcium progression after coronary artery bypass grafting surgery
Bibliographic record
Abstract
OBJECTIVES: Accelerated atherosclerosis is a well-established phenomenon after coronary artery bypass grafting surgery (CABG). In this study, we analysed coronary artery calcium (CCS) progression after CABG. METHODS: We retrospectively measured the CCS Agatston score (AS), volume score (VS) and mass score (MS) of 39 patients before and after CABG. The annualised CCS change and annualised CCS percent change of each coronary artery, coronary artery segments proximal and distal to anastomosis were analysed. RESULTS: Mean age at the time of the surgery was 59.8±8.5 years. Follow-up period between the first and second CT scans was 6.7±2.8 (range, 1.1-12.8) years. Annualised CCS percent change (AS, VS and MS) of the coronary segments proximal-to-anastomosis did not differ from that of the non-grafted coronary arteries as follow: segments proximal-to-anastomosis: median (Q1-Q3) 12.8 (5.0-37.4), 13.7 (6.1-41.1) and 14.9 (5.4-53.7), left main coronary artery 12.6 (7.4-43.8), 22.0 (8.1-44.4) and 18.2 (7.3-57.4), non-grafted left circumflex artery: 13.5 (4.4-38.1), 10.5 (2.9-45.2) and 11.5 (7.1-47.9) and non-grafted right coronary artery: 31.4 (14.4-74.5), 25.2 (16.7-62.0) and 31.3 (23.8-85.6), respectively. Likewise, annualised percent change (AS, VS and MS) was similar between the native coronary arteries. Multivariate regression analysis showed that diabetes mellitus was the only predictor of annualised percent progression of the total CCS of >15% (HR, 8.12; 95% CI, 1.05 to 26.6; p=0.04). CONCLUSION: The CCS post-CABG did not follow an accelerated progression process. Among coronary artery disease risk factors, diabetes mellitus is the only predictor of annualised CCS percent progression of >15% post-CABG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".