Programming strategies to reduce non-essential therapies and mortality - a systematic review
Bibliographic record
Abstract
Purpose: Implantable cardioverter defibrillator (ICD) therapies are linked to a poor outcomes. Hence, programming strategies to reduce non-essential ICD therapies (Tx reduction programming) have been developed. This systematic review and meta-analysis of relevant studies sought to quantify the impact of Tx reduction programming on mortality. Methods: MEDLINE, EMBASE and other online databases were searched to identify relevant articles using standard methods. Studies that assessed mortality and followed patients for at least 6 months were included. Results were abstracted in duplicate and analyzed using Stata (v 11) statistical software using randomized effects models. Results: Five trials met the inclusion criteria, 3 with (EMPIRIC, MADIT-RIT & ADVANCE III) and 2 without (PREPARE & RELEVANT), a randomized comparator group. The 5 studies included 6,017 (2,774 conventional & 3,243 Tx reduction programming) patients. Most (79%) participants were male, had a history of ischemic heart disease (57%), and were prescribed beta-blockers (82%). Tx reduction programming reduced mortality by 33% (95% Confidence Interval (CI) 17% to 46%) as compared with conventional programming (FIGURE). No significant heterogeneity among the studies was observed (p = 0.6). Similar reductions in mortailty were observed when only the 3 randomized trials were included (29% reduction, 95% CI 8% to 45%) and when the 2 active therapy MADIT RIT groups were combined and compared to the 1 control group (32% reduction, 95% CI 15% to 46%). Figure 1 Figure 1 Conclusions: Therapy reduction programming is associated with a large, significant and consistent reduction in mortality. The reasons for this reduction in mortality are unclear and merit further study.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".