Magna Ease versus Trifecta Early Hemodynamics
Bibliographic record
Abstract
OBJECTIVE: This meta-analysis compares the early echocardiographic outcomes of aortic valve replacement using the two most commonly implanted stented bioprostheses. METHODS: We searched MEDLINE and EMBASE databases until 2017 for studies comparing Magna or Magna Ease (Edwards Lifesciences, Irvine, CA USA) versus Trifecta (St Jude Medical, St. Paul, MN USA) aortic bioprosthetic valves. A random-effects meta-analysis was performed for the primary outcome of mean gradient on echocardiography and secondary outcomes of effective orifice area, indexed effective orifice area, and in-hospital mortality. RESULTS: There were two randomized controlled trial, three matched, and six unmatched retrospective observational studies with 2119 patients [median reported follow-up = 6 months (interquartile range = 6 to 12)]. The Magna/Magna Ease valve was associated with higher early mean gradient (mean difference = 4.09, 95% confidence interval = 3.48 to 4.69, P < 0.0001) and smaller effective orifice area (mean difference = 0.30, 95% confidence interval = -0.38 to -0.22, P < 0.0001). There were no differences in 30-day mortality between Magna/Magna Ease and Trifecta (relative risk = 1.01, 95% confidence interval = 0.41 to 2.50, P = 1.0). CONCLUSIONS: Trifecta may offer a small hemodynamic advantage compared with the Magna/Magna Ease valve with no differences in early mortality. Long-term follow-up is required to determine whether these differences persist and translate into differences in clinical outcomes.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.017 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".