A Systematic Review of Contemporary Randomized Trials in Cardiothoracic Surgery
Bibliographic record
Abstract
Background: This analysis was conducted to characterize contemporary randomized controlled trials (RCTs) in cardiothoracic surgery. Methods: We selected randomized controlled trials published in the journals with the highest impact factor in medicine, general surgery, and cardiothoracic surgery and published between 2008 and 2020. Trial characteristics as well as measures of reporting and quality were summarized and compared. Results: Ninety-three trials were included; 44 (47.3%) were prospectively registered and 14 (31.8%) had a discrepancy between the registered and published primary outcome. Most trials (n = 83 [89.1%]) used a superiority design, a composite primary outcome (n = 82 [88.2%]), and a major clinical event as the primary end point (n = 67 [72.0%]). Blinding was used infrequently, and most trials did not control for surgeon experience (n = 74 [79.5%]) or monitor the intervention (n = 90 [96.7%]). Twenty-four (25.8%) trials had high risk of bias. Twenty-one (27.3%) trials were funded by industry. A median 1.62% of patients (interquartile range, 0.00-3.70) crossed over between trial arms. Most trials reported a favorable outcome (n = 53 [58.9%]). For eligible trials, the median fragility index was 2.0 (interquartile range, 0.0-4.0), meaning the change of 2 patient outcomes would render the significant result insignificant. Spin, or distortion in reporting, was identified in 9 of 53 trials (17.0%). The median number of citations was 25 (10-56). Conclusions: Contemporary trials in cardiothoracic surgery are pragmatic with low rates of loss to follow-up and crossover. Few trials implemented measures to ensure quality of the intervention, and the presence of spin was infrequent.
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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 | 0.123 | 0.369 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.012 |
| Bibliometrics | 0.032 | 0.030 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 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".