Randomized Trials in Cardiac Surgery: Why and How
Bibliographic record
Abstract
OBJECTIVES: Randomized clinical trials (RCTs) are the gold standard for comparative effectiveness. However, they face unique challenges in cardiac surgery. The objective of this work is to summarize the challenges of RCTs in cardiac surgery, describe efforts employed in recent years to mitigate these impediments and outline the future opportunities for increased RCT adoption in the specialty. METHODS: This review was conducted as an expert analysis on the existing state of RCTs in cardiac surgery based on expert discussion at a dedicated session during the 2024 Annual Meeting of the European Association for Cardio-Thoracic Surgery (EACTS). Different trial-support infrastructures, such as the Randomized Comparison of the Clinical Outcomes of Single versus Multiple Arterial Grafts (ROMA) Network, the Cardiothoracic Surgical Trials Network (CTSN), the Global Cardiovascular Research Funders Forum (GCRFF) and the UK Model, and their respective mechanisms for overcoming RCT barriers were described in detailed. Models were selected due to specific author involvement and knowledge. Future directions were postulated based on current trends. RESULTS: Despite heterogeneous structures, the described models largely aimed to increased cardiac RCTs through improved trial participation, either via increased trainees, expanded stakeholders or focused patient recruitment, facilitating funding and fostering wider collaboration. CONCLUSIONS: RCTs are a key component for clinical advancement yet have been underutilized in cardiac surgery. Recent endeavours have reduced the multifactorial barriers associated with cardiac surgery RCTs and intentional future efforts are necessary for continued cardiac advancement.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.442 | 0.679 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".