Heart Rate Variability in Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Heart rate variability (HRV) is a noninvasive measure of cardiac autonomic modulation. Time and frequency domain measures have primarily been examined in patients with type 2 diabetes mellitus (T2D). Not only do frequency domain HRV parameters tend to be reduced in T2D, but healthy individuals with low HRV are also more likely to develop T2D. Furthermore, patients with T2D with low HRV have an increased prevalence of complications and risk of mortality compared with those with normal autonomic function. These findings provide support for the use of HRV as a risk indicator in T2D. Exercise is considered an important component to T2D prevention and treatment strategies. To date, few studies have examined the changes in HRV with exercise in T2D. One study showed changes in resting HRV, two studies showed changes in HRV during or following acute stressors, and one study showed no changes in HRV but improvements in baroreflex sensitivity. The most pronounced changes in HRV were realized following the exercise intervention with the greatest frequency of supervised exercise sessions and with the greatest intensity and duration of exercise bouts. These findings suggest that exercise following current American College of Sports Medicine/American Diabetes Association guidelines may be important in the prevention and treatment of T2D to improve autonomic function and reduce the risk of complications and mortality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".