Heart transplantation versus left ventricular assist devices as destination therapy or bridge to transplantation for 1-year mortality: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The optimal treatment for advanced heart failure (HF) patients with regards to mortality remains unknown. Heart transplantation (HTx) and left ventricular assist devices (LVAD) used either as a bridge to transplant (BTT) or destination therapy (DT) have been compared in a number of studies, without definite conclusions with regards to mortality benefit. We sought to systematically review the pertinent literature and perform a meta-analysis of all the available studies presenting head-to-head comparisons between HTx and LVAD BTT or LVAD DT for late (>6 months) all-cause mortality. METHODS: DT LVAD using a random effects model. RESULTS: Eight studies were included in our meta-analysis, reporting data on 7,957 patients in total. Although the available studies are of high quality [8 stars in Newcastle-Ottawa Scale (NOS) on average], there is paucity of mortality data. Specifically, seven studies compared HTx with BTT and five studies compared HTx with DT for 1-year mortality. Our pooled estimates showed that there was no difference in late mortality among these strategies. CONCLUSIONS: Our meta-analysis highlights the small number and the heterogeneity of available studies referring to the optimal invasive management of advanced HF, and shows that there are no differences between HTx and LVAD for these patients with regards to late mortality.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.045 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".