Comparative Accuracy of Ambulatory ElectrocardiographicAnalysis Systems
Bibliographic record
Abstract
Using a beat-to-beat hand count as the true diagnostic standard, the accuracy of 3 Holter systems in the analysis of 15-min electrocardiograms from 20 patients with frequent ventricular ectopic beats (VEBs) and 20 normal control subjects was assessed. The sensitivity of all 3 systems for VEB detection was high (92, 93 and 95%), and there were no differences (NS) in the absolute hand and system counts of total beats or VEBs, nor calculations of mean, maximum and minimum prevailing rates. Percent errors of total beats and mean and maximum prevailing rates were, on average, small (range 1 ± 2 to 3 ± 2%) and similar (NS) among the 3 systems. Percent errors of minimum rate were higher and significantly different (p < 0.01) among systems (10 ± 4 versus 4 ± 3 versus 2 ± 3%). Average VEB errors were even greater (10 ± 14, 8 ± 24 and 4 ± 6%), and there were no intersystem differences (NS). Individual intrasystem VEB errors ranged from 0 to 100%. Thus, commercial Holter systems provide accurate analysis of total beats and most prevailing rate parameters, but a considerably less accurate assessment of VEB frequency. However, enhanced performance may be obtained if specific systems are matched to specific clinical or research objectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".