Effect of Implantable Defibrillators on Arrhythmic Events and Mortality in the Multicenter Unsustained Tachycardia Trial
Bibliographic record
Abstract
BACKGROUND: The Multicenter Unsustained Tachycardia Trial (MUSTT) was designed to evaluate an antiarrhythmic treatment strategy, including drugs and implantable defibrillators (ICDs), guided by electrophysiological (EP) testing. We performed several statistical analyses to assess the contribution of defibrillators to the observed treatment benefit. METHODS AND RESULTS: First, the effects of defibrillators were indirectly examined by comparing the randomized treatment arms (EP-guided therapy versus no antiarrhythmic therapy) within subgroups that varied according to ICD usage. Use of ICDs increased during the trial; hence, the randomized treatments were compared according to date of enrollment. There were also site-specific differences in ICD use; hence, the randomized arms were compared within groups of sites defined by level of ICD use. There was a distinct "dose response" in relation to ICD use. Where ICD use was high, EP-guided therapy produced significant reductions in arrhythmic death or cardiac arrest (P<0.004). Where ICD use was low, there was no benefit of EP-guided therapy. Finally, outcomes of EP-guided therapy patients who received an ICD were directly compared with outcomes of other patients using the Cox proportional hazards model with receipt of an ICD as a time-dependent covariate. Adjusted for other prognostic factors, patients who received an ICD had risk reductions of >70% in arrhythmic death or cardiac arrest and >50% in total mortality (P<0.001 for both end points). CONCLUSIONS: The benefit of EP-guided antiarrhythmic therapy observed in MUSTT was due to improved outcomes among patients who received an ICD but not among patients who received antiarrhythmic drugs.
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".