Cardiac Autonomic Neuropathy: Why Should Cardiologists Care about That?
Bibliographic record
Abstract
BACKGROUND: Cardiac autonomic neuropathy (CAN) is a frequent but underdiagnosed complication of diabetes mellitus. It has a strong influence on various cardiac disorders including myocardial ischemia and infarction, hypertension, orthostatic hypotonia, heart failure, and arrhythmias. CAN can lead to severe morbidity and mortality and increase the risk of sudden cardiac death. METHODS: This review article summarizes the latest evidence regarding the epidemiology, pathogenesis, influence on the cardiovascular system, and diagnostic methods for CAN. The methodology of this review involved analyzing available data from recent papers relevant to the topic of diabetic autonomic neuropathy and cardiac disorders. CONCLUSIONS: The early diagnosis of CAN can improve the prognosis and reduce adverse cardiac events. Methods based on heart rate variability enable the diagnosis of CAN even at a preclinical stage. These methods are simple and widely available for use in everyday clinical practice. According to the recently published Toronto Consensus Panel on Diabetic Neuropathy, all diabetic patients should be screened for CAN. Because diabetes mellitus often coexists with heart diseases and the most common methods used for diagnosis of CAN are based on ECG, not only diabetologists but also cardiologists should be responsible for diagnosis of CAN.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".