Field validity of heart rate variability metrics produced by QRSTool and CMetX.
Bibliographic record
Abstract
Interest in heart rate variability (HRV) metrics as markers of physiological and psychological health continues to grow beyond those with psychophysiological expertise, increasing the importance of developing suitable tools for researchers new to the field. Allen, Chambers, and Towers (2007) developed QRSTool and CMetX software as simple, user-friendly tools that can be used to compute metrics of HRV. In the present study, the authors examined the field validity of these software tools--that is, their validity when used by nonexperts. In a lab with extensive expertise in psychopathology but not psychophysiology, ECG data were obtained from 63 undergraduates at baseline and during a stressor and then processed using QRSTool and CMetX to produce the 10 HRV indices described in Allen et al. (2007). The indices displayed factor structures and patterns of changes from baseline to stressor that were similar to findings from Allen et al. and consistent with how indices of parasympathetic and sympathetic activity should behave. Results support the field validity of QRSTool and CMetX, suggesting that they are useful for nonexperts in psychophysiology interested in measuring HRV.
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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 | 0.004 | 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) | 0.001 | 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 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".