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Multicenter Automatic Defibrillator Implantation Trial: Reduce Inappropriate Therapy (MADIT‐RIT): Background, Rationale, and Clinical Protocol

2012· article· en· W1998021054 on OpenAlexaboutno aff
Claudio Schuger, James P. Daubert, Mary W. Brown, David S. Cannom, N.A. Mark Estes, William J. Hall, Torsten Kayser, Helmut Klein, Brian Olshansky, Keith A. Power, David J. Wilber, Wojciech Zaręba, Arthur J. Moss

Bibliographic record

VenueAnnals of Noninvasive Electrocardiology · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsnot available
FundersMedical Center, University of RochesterUniversity of RochesterBoston Scientific CorporationSanofi
KeywordsMedicineImplantable cardioverter-defibrillatorRandomized controlled trialInternal medicineClinical trialMulticenter trialCardiologyIntensive care medicineMulticenter study

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.141
GPT teacher head0.433
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2012
Admission routes1
Has abstractyes

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