Relation between detection rate and inappropriate shocks in single versus dual chamber cardioverter-defibrillator – an analysis from the OPTION trial
Bibliographic record
Abstract
The programming of implantable cardioverter-defibrillators (ICDs) influences inappropriate shock rates. The aim of the study is to analyse rates of patients with appropriate and inappropriate shocks according to detection zones in the OPTION trial. All patients received dual chamber (DC) ICDs randomly assigned to be programmed either to single chamber (SC) or to DC settings including PARAD+ algorithm. In a post-hoc analysis, rates of patients with inappropriate and appropriate shocks were calculated for shocks triggered at heart rates ≥ 170 bpm (ventricular tachycardia zone) and at rates ≥ 200 bpm (ventricular fibrillation zone). In the SC group, higher rates of patients with total and inappropriate shocks were delivered at heart rates ≥ 170 bpm than at rates ≥ 200 bpm (total shocks: 21.1% vs. 16.6%; p = 0.002; inappropriate shocks: 7.6% vs. 4.5%, p = 0.016; appropriate shocks: 15.2% vs. 13.5%; p = n.s.). No such differences were observed in the DC group (total shocks: 14.3% vs. 12.6%; p = n.s.; inappropriate shocks: 3.9% vs. 3.6%; p = n.s.; appropriate shocks: 12.2% vs. 10.4%; p = n.s.). The higher frequency of patients with total shocks with SC settings than with DC settings that benefit from PARAD+ was driven by a higher percentage of patients with inappropriate shocks in the VT zone (170-200 bpm) in the SC population.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".