Predictors of Cardiovascular Autonomic Neuropathy Onset and Progression in a Cohort of Type 1 Diabetic Patients
Bibliographic record
Abstract
Aim. The prevalence of cardiovascular autonomic neuropathy (CAN) in diabetes mellitus is well documented. However, the rate and predictors of both the development and progression of CAN have been less studied. Hereby, we assessed the rate and the major risk factors for CAN initiation and progression in a cohort of type 1 diabetic patients followed over a three-year period. Methods. 175 type 1 diabetic patients (mean age: 50 ± 11 years; female/male: 76/99) with positive bedside screening for CAN were included and underwent 2 standardized autonomic testings using 4 standardized tests (deep breathing, Valsalva maneuver, 30/15 ratio, and changes in blood pressure during standing), separated by 3 ± 1 years. CAN staging was achieved according to the Toronto Consensus Panel on Diabetic Autonomic Neuropathy into 4 categories: absent, possible, confirmed, or severe CAN. Results. Out of the 175 patients included, 31.4% were free of CAN, 34.2% had possible CAN, 24.6% had confirmed CAN, and 9.7% exhibited severe CAN at the first assessment. Among the 103 patients with nonsevere CAN at inclusion, forty-one (39.8%) had an increase of at least one category when reassessed and 62 (60.2%) remained stable. A bivariate analysis indicated that only BMI and exposure to selective serotonin reuptake inhibitors (SSRIs) were significantly different in both groups. A multivariate analysis indicated that lower BMI (OR: 0.15, CI 95%: 0.05–0.48, p=0.003 ) and SSRI exposure (OR: 4.18, CI 95%: 1.03–16.97, p=0.04 ) were the sole predictors of CAN deterioration. In the 55 patients negative for CAN at the first laboratory assessment, 12 became positive at the second assessment. Conclusion. No clear predictive factor for CAN onset was identified. However, once present, CAN progression was related to low BMI and SSRI exposure.
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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 | 0.001 | 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) | 0.001 | 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".