Cardiovascular autonomic neuropathy in diabetes: clinical impact, assessment, diagnosis, and management
Bibliographic record
Abstract
The Cardiovascular Autonomic Neuropathy (CAN) Subcommittee of the Toronto Consensus Panel on Diabetic Neuropathy worked to update CAN guidelines, with regard to epidemiology, clinical impact, diagnosis, usefulness of CAN testing, and management. CAN is the impairment of cardiovascular autonomic control in the setting of diabetes after exclusion of other causes. The prevalence of confirmed CAN is around 20%, and increases up to 65% with age and diabetes duration. Established risk factors for CAN are glycaemic control in type 1 and a combination of hypertension, dyslipidaemia, obesity, and glycaemic control in type 2 diabetes. CAN is a risk marker of mortality and cardiovascular morbidity, and possibly a progression promoter of diabetic nephropathy. Criteria for CAN diagnosis and staging are: (1) one abnormal cardiovagal test result identifies possible or early CAN; (2) at least two abnormal cardiovagal test results are required for definite or confirmed CAN; and (3) the presence of orthostatic hypotension in addition to abnormal heart rate test results identifies severe or advanced CAN. Progressive stages of CAN are associated with increasingly worse prognosis. CAN assessment is relevant in clinical practice for (1) diagnosis of CAN clinical forms, (2) detection and tailored treatment of CAN clinical correlates (e.g. tachycardia, orthostatic hypotension, non-dipping, QT interval prolongation), (3) risk stratification for diabetic complications and cardiovascular morbidity and mortality, and (4) modulation of targets of diabetes therapy. Evidence on the cost-effectiveness of CAN testing is lacking. Apart from the preventive role of intensive glycaemic control in type 1 diabetes, recommendations cannot be made for most therapeutic approaches to 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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".