Clinical trials in valvular surgery
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is a growing emphasis on the conduct of large-scale, multicenter randomized controlled trials (RCTs) to guide decision-making in cardiac surgery. Here we review recent landmark RCTs in cardiac valvular surgery. RECENT FINDINGS: RCTs are the gold-standard level of data in medicine. However, there are unique challenges of conducting large-scale surgical trials including funding, blinding, generalizability, nonstandardization of the surgical technique, crossover, among others. Thus, the vast majority of clinical outcomes data in cardiac surgery are mainly from observational studies and most prospective data are small, single-center trials. The Cardiothoracic Surgery Network is the largest platform focused on the conduct of high-quality, multicenter cardiac surgical trials, which has already produced several seminal guideline-changing and practice-changing contributions to the surgical approach to functional mitral regurgitation, aortic stenosis, atrial fibrillation, and neuroprotective surgical adjuncts. SUMMARY: There continues to be great interest in the conduct of high-quality, RCTs to help guide surgical management of patients with valvular heart disease.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".