Multidisciplinary Teams for the Management of Infective Endocarditis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: The management of infective endocarditis (IE) is complex owing to a high burden of morbidity and mortality. Recent guidelines recommend dedicated multidisciplinary teams (MDTs) for the management of IE. The aim of this systematic review and meta-analysis was to evaluate and summarize the effect of MDT management on patient outcomes. Methods: A systematic review was performed and, where feasible, results were meta-analyzed; otherwise, results were summarized narratively. Data extraction and quality assessment were performed in duplicate. Restricted maximum likelihood random effects models were used to calculate unadjusted risk ratios and 95% CIs. Results: = 62%) for mortality in favor of a dedicated MDT as compared with usual care. Length of stay was variable, with 55% (10/18) of studies reporting an increased length of stay. Most studies (16/18, 88.9%) reported a decreased time to surgery and an increased rate of surgery (13/18, 73%). No studies reported on patient-reported outcomes. Conclusions: This is the first systematic review and meta-analysis to assess the impact of MDT management on IE. The sum of evidence demonstrated a significant association between MDTs and improved short-term mortality. Further research is needed to evaluate benefits of virtual MDT care, cost-effectiveness, and the impact on patient-reported outcomes and long-term 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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.006 | 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".