Comparison of ICD shock rates in Japanese and non‐Japanese patients in the PainFree SST study
Bibliographic record
Abstract
BACKGROUND: The PainFree Smart Shock Technology (SST) study showed a low implantable cardioverter-defibrillator (ICD) inappropriate shock rate. However, the majority of patients were from Western countries with patient characteristics different from those in Japan. ICD shock rates using the novel SST algorithms in Japanese patients are still unknown. METHODS: All 2,770 patients in the PainFree SST study (Japan [JPN]: N = 181, other geographies [OJPN]: N = 2,589) were included in this analysis. RESULTS: Japanese patients had higher average left ventricular ejection fraction (P < 0.0001), higher prevalence of secondary prevention indications (P < 0.0001), nonischemic cardiomyopathy (P < 0.0001), and permanent atrial fibrillation (P < 0.0001). The appropriate shock rate at 12 months was not different between JPN and OJPN: 6.4% and 6.3%, respectively (P = 0.95). The inappropriate shock rate at 12 months was significantly higher in Japanese patients (2.9% vs 1.7%, P = 0.017). However, after propensity score matching to adjust for the difference in baseline characteristics, the difference in inappropriate shock rate was not statistically significant (P = 0.51). CONCLUSIONS: There was no difference in the appropriate shock rate between Japan and other geographies. The inappropriate shock rate in Japan was low, although it was slightly higher compared to other geographies due to baseline characteristics, including a higher prevalence of permanent AF. There was not a statistically significant difference after adjusting for baseline characteristics.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".