Diagnostic reliability of monitoring for premature atrial and ventricular complexes
Bibliographic record
Abstract
AIMS: Short-term ambulatory electrocardiogram (ECG) monitoring is often used to assess premature atrial complex (PAC) and premature ventricular complex (PVC) frequency, but the diagnostic reliability is unknown. The objective of this study was to study the day-to-day variability of PAC and PVC frequency. METHODS AND RESULTS: We used 14-day full-disclosure mobile cardiac telemetry recordings without atrial fibrillation in 8245 US patients aged 17-103 years to calculate the diagnostic reliability of shorter ambulatory ECG recordings compared with 14-day averages. Over 14 days, 1853 patients had ≥500 PACs/day, 410 patients had ≥5000 PACs/day, and 197 patients had ≥10 000 PACs/day; 1640 patients had ≥500 PVCs/day, 354 patients had ≥5000 PVCs/day, and 175 patients had ≥10 000 PVCs/day. After 3 days, the estimated daily PAC frequency differed by ≥50% from the 14-day mean in 25% of patients; for PVCs, the corresponding duration was 7 days. Ten days of monitoring were needed to estimate PAC and PVC frequency within ±20% of the overall 14-day frequency in 80% of patients. For daily PAC and PVC frequencies ≥10 000, single-day estimation had a specificity of 99.3% [95% confidence interval (CI) 99.1-99.5] at a sensitivity of 76.6 (95% CI 70.1-80.4%) for PACs and a 99.6% (95% CI 99.4-99.7%) specificity at 79.4 (95% CI 72.7-85.2) sensitivity for PVCs. After 7 days, the sensitivity increased to 88.8% (95% CI 83.6-92.9) for PACs and 86.9% (95% CI 80.9-91.5%) for PVCs. CONCLUSION: While there is substantial daily variability across most PAC and PVC levels, findings of ≥10 000 PACs or PVCs are highly specific and do not need to be confirmed with longer recordings.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".