Relationship between Beat‐to‐Beat Variability of RT‐Peak and RT‐End Intervals in Normal Controls, Patients with Anxiety, and Patients with Cardiovascular Disease
Bibliographic record
Abstract
BACKGROUND: Some studies suggest that it is important to take the end of "T" wave to quantify QT-interval variability, which signifies cardiac repolarization lability, as there is substantial and important information beyond the peak of the T wave on the surface electrocardiogram. METHODS: In this study, we examined the relationship between the variability of beat-to-beat RTe (beginning of R-peak to T-end) and the variability of RTp (R-peak to T-peak) in the following groups: normal controls (n = 26), patients with anxiety (n = 26), and patients with cardiovascular disease with or without diabetes (n = 63). We obtained ECG sampled at 1024 Hz in lead II configuration in supine posture to obtain beat-to-beat interbeat interval (R-R) and RT-interval variability for 256 seconds. RESULTS: We found significant positive correlations (r = 0.8; P < 0.00001) in normal controls and patients with anxiety between the variability of RTeVI and RTpVI (RTe and RTp variability indices, respectively, corrected for the mean of RTe and RTp and the mean and the variance of R-R). These correlations were also statistically significant in the medically ill group but the r values were much smaller (r = 0.45 in various groups). The slopes were also significantly different between the two groups (P < 0.001). Bland-Altman plots also showed better agreement between the two measures in the controls and patients with anxiety compared to the group with cardiovascular disease. CONCLUSIONS: These findings have methodological implications for studies comparing people with and without overt cardiovascular illness. While RTe or RTp variability index may be used interchangeably in normal controls and some patients with no overt cardiovascular problems, it may be more prudent to use both RTe and RTp variability indices in patients with cardiovascular illness. These indices, especially RTeVI, may provide different information about cardiac repolarization lability. Future studies should address the importance of the relative usefulness of these two measures especially in cardiac patients before and after successful treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".