Nonlinear broad band dynamics are less complex in major depression
Bibliographic record
Abstract
OBJECTIVES: Cardiac mortality is known to be increased in depressive patients. However, the underlying mechanisms remain elusive to date. Decreased heart rate variability (HRV) has been discussed as contributing to increased cardiac mortality, but studies examining patients suffering from major depressive disorder (MDD) have revealed inconsistent results. This study aimed to investigate long-term and broad band parameters of heart rate regulation in MDD, which have been shown to be more sensitive for the assessment of autonomic dysfunction. METHODS: A total of 18 non-medicated patients suffering from MDD and 18 matched control subjects without cardiac disease were recruited and 24-h ambulatory electrocardiograms were recorded. Data were recorded during three distinct time intervals linear and nonlinear parameters as well as autonomic information flow (AIF) were calculated. RESULTS: The power law slope was significantly reduced in the patient group for all intervals investigated and correlated with symptom severity, whereas standard deviation of the 5-min NN intervals (SDANN) and area under the AIF curve (INT(NN)) showed significant differences between groups in the morning hours only. Analysis of standard HRV parameters in the time and frequency domain revealed no significant differences between groups. CONCLUSIONS: The evidence for decreased complexity of cardiac regulation in depressed patients presented here might be useful as an indicator of the increased cardiac mortality known in depression, especially since these parameters are capable of predicting cardiac mortality in other diseases. The importance of these parameters for patients at risk should be evaluated in future prospective studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".