Multicenter Automatic Defibrillator Implantation Trial: Reduce Inappropriate Therapy (MADIT‐RIT): Background, Rationale, and Clinical Protocol
Bibliographic record
Abstract
The implantable cardioverter defibrillator (ICD) is highly effective in reducing mortality due to cardiac arrhythmias in high-risk cardiac patients. However, inappropriate therapies caused predominantly by supraventricular tachyarrhythmias (SVTs) remain a significant side effect of ICD therapy despite medical treatment, affecting 8-40% of patients. The MADIT-RIT is a global, prospective, randomized, nonblinded, three-arm, multicenter clinical investigation to be performed in the Unites States, Europe, Canada, Israel and Japan, and will utilize approximately 90 centers with plan to enroll 1500 patients programmed to three treatment arms. The objective of the MADIT-RIT trial is to determine if dual-chamber ICD or CRT-D devices with high rate cutoff (MADIT-RIT-Arm B) and/or long delay in combination with detection enhancements (MADIT-RIT-Arm C) are associated with fewer patients experiencing inappropriate therapies than standard programming (MADIT-RIT-Arm A) during postimplant follow-up of patients with indication for primary prevention device therapy. This paper describes design and analytic plan for the MADIT-RIT trial.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".