Distribution of Patients′ Paroxysmal Atrial Tachyarrhythmia Episodes: Implications for Detection of Treatment Efficacy
Bibliographic record
Abstract
INTRODUCTION: Clinical trials of treatments for paroxysmal atrial tachyarrhythmia (pAT) often compare different treatment groups using the time to first episode recurrence. This approach assumes that the time to the first recurrence is representative of all times between successive episodes in a given patient. We subjected this assumption to an empiric test. METHODS AND RESULTS: Records of pAT onsets from a chronologic series of 134 patients with dual chamber implantable defibrillators were analyzed; 14 had experienced >10 pAT episodes, which is sufficient for meaningful statistical modeling of the time intervals between episodes. Episodes were independent and randomly distributed in 9 of 14 patients, but a fit of the data to an exponential distribution, required by the stated assumption, was rejected in 13 of 14. In contrast, a Weibull distribution yielded an adequate goodness of fit in 5 of the 9 cases with independent and randomly distributed data. Monte Carlo methods were used to determine the impact of violations of the exponential distribution assumption on clinical trials using time from cardioversion to first episode recurrence as the dependent measure. In a parallel groups design, substantial loss of power occurs with sample sizes <500 patients per group. In a cross-over design, there is insufficient power to detect a 30% reduction in episode frequency even with 300 patients. CONCLUSION: Clinical trials that rely on time to first episode recurrence may be considerably less able to detect efficacious treatments than may have been supposed. Analysis of multiple episode onsets recorded over time should be used to avoid this pitfall.
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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 | 0.043 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".