Dependence of premature ventricular complexes on heart rate ---it's not that simple
Bibliographic record
Abstract
IntroductionFrequent premature ventricular complexes (PVCs) can lead to adverse health conditions such as cardiomyopathy.The linear correlation between PVC frequency and heart rate (as positive, negative, or neutral) has been proposed as a measure to guide treatment with beta-blockers.We evaluate the robustness of this measure to day-to-day variability and measurement methodology. MethodsWe analyzed 82 multi-day ECG recordings collected from 48 patients with frequent PVCs (burden 1-44%).For each record, the linear correlation between PVC frequency and heart rate was computed for different 24-hour periods, and using different time interval lengths to determine the PVC frequency and average heart rate. ResultsUsing a 1-hour time interval, the correlation between PVC frequency and heart rate was consistently positive, negative or neutral on different days in 19.5% of patients.Using shorter time intervals, the correlation was consistent in 34.1-58.5% of patients.Using 1-minute time intervals emphasized a nonlinear dependence of PVC frequency on heart rate in most patients. ConclusionIn patients with frequent PVCs, linear correlation of PVC frequency with heart rate is variable across different 24-hour periods and different interval lengths used to compute the average heart rate.The variable and often nonlinear dependence of PVC frequency on heart rate suggests that classification based on linear correlation should be used with caution.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".