An investigation of cardiac indices in normal sleepers and those with insomnia disorder
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the world’s leading cause of death. Insomnia, a prevalent disorder among Canadian adults, has been identified in some studies as an independent risk factor for CVD. Heart rate variability (HRV), often used as a proxy for autonomic activity in the body, has been demonstrated to be impaired in individuals with insomnia. Scientists have suggested that results implicate exaggerated sympathetic activation in people with insomnia, which can lead to impaired cardiac reactivity and poorer heart health. Much of the research in this field has been with poorly classified clinical groups and potentially confounding comorbid disorders, making the results difficult to interpret. Findings of the present study extend our understanding of the relationship between insomnia and CVD by addressing the weaknesses of prior research and by utilizing contemporary statistical methods. A well-classified clinical group meeting Research Diagnostic Criteria for Insomnia Disorder (ID; N = 26) was compared to normal sleepers (NS; N = 23) on two well-validated indices of cardiovascular health and autonomic activity, cardiac output (CO) and pre-ejection period (PEP). Values of these indices were derived from an acoustic challenge paradigm that allowed the heart to adapt to the environment via autonomic influence. A multi-level modeling (MLM) approach was used to evaluate both the within-person and between-person differences in autonomic activation and cardiac functioning using a group of predictors that are known to be associated with insomnia and/or CVD. Results of the level two MLM analyses revealed that sleep variables are not significantly predictive of cardiac reactivity indices. A post-hoc linear regression analysis using the same predictor variables to predict HRV revealed that insomnia was significantly predictive of HRV. These conflicting results raise important questions about research methodology, validity of chosen indices, and statistical techniques.
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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 | 0.000 |
| 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) | 0.001 | 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".