Association between Cardiac Autonomic Neuropathy and Coronary Artery Lesions in Patients with Type 2 Diabetes
Bibliographic record
Abstract
Objective. Cardiac autonomic neuropathy (CAN) is a common and serious complication of diabetes mellitus with various systemic involvements, such as atherosclerotic cardiovascular disease. We aimed to evaluate the association between CAN and coronary artery lesions in patients with type 2 diabetes. Research Design and Methods. We retrospectively reviewed the medical records of 104 patients with type 2 diabetes and coronary artery disease (CAD). We evaluated heart rate variability (HRV) parameters (SDANN, SDNN, and pNN50) to assess cardiac autonomic function. The severity of coronary lesions was assessed by the Gensini scores and the number of affected vessels. Correlation analyses between HRV parameters and the severity of coronary lesions and clinical parameters were performed. Results. Spearman’s correlation analysis showed a significant negative correlation between SDANN and Gensini scores ( r=−0.22 , P=0.03 ). Interestingly, this finding remained significant after adjusting for clinical covariates ( r=−0.23 , P=0.03 ). However, there was no association between HRV parameters and the severity of coronary lesions as assessed by the number of affected vessels. Clinical parameters were not significantly correlated with HRV parameters (all P>0.05 ). Conclusions. Cardiac autonomic neuropathy might be related to the degree of coronary atheromatous burden in patients with type 2 diabetes. Screening for cardiac autonomic neuropathy might potentially be beneficial in the risk stratification of patients with type 2 diabetes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".