Cardiovascular autonomic neuropathy in patients with type 2 diabetes with and without sensorimotor polyneuropathy
Bibliographic record
Abstract
BACKGROUND AND AIMS: Cardiovascular autonomic neuropathy (CAN) in patients with diabetes is associated with poor prognosis. We aimed to assess signs of CAN and autonomic symptoms and to investigate the impact of sensorimotor neuropathy on CAN by examining type 2 diabetes patients with (DPN [distal sensorimotor polyneuropathy]) and without distal sensorimotor polyneuropathy (noDPN) and healthy controls (HC). Secondarily, we aimed to describe the characteristics of patients with CAN. METHODS: A population of 374 subjects from a previously described cohort of the Danish Centre for Strategic Research in Type 2 Diabetes (DD2) were included. Subjects were examined with the Vagus™ device for the diagnosis of CAN, where two or more abnormal cardiovascular autonomic reflex tests indicate definite CAN. Autonomic symptoms were assessed with Composite Autonomic Symptom Score 31 (COMPASS 31) questionnaire. DPN was defined according to the Toronto consensus panel definition. RESULTS: Definite CAN was present in 22% with DPN, 7% without DPN and 3% of HC, and 91% of patients with definite CAN had DPN. Patients with DPN and definite CAN reported higher COMPASS 31 scores compared to patients with noDPN (20.0 vs. 8.3, p < 0.001) and no CAN (22.1 vs. 12.3, p = 0.01). CAN was associated with HbA1c and age in a multivariate logistic regression analysis but was not associated with IEFND or triglycerides. INTERPRETATION: One in five patients with DPN have CAN and specific CAN characteristics may help identify patients at risk for developing this severe diabetic complication. Autonomic symptoms were strongly associated with having both DPN and CAN, but too unspecific for diagnosing 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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".