Prevalence and Impact of Treatment Crossover in Cardiac Surgery Randomized Trials: A Meta‐Epidemiologic Study
Bibliographic record
Abstract
Background Crossover dilutes treatment effect and reduces statistical power of intention-to-treat analysis. We examined incidence and impact on cardiac surgery randomized controlled trial (RCT) outcomes of crossover from experimental to control interventions, or vice versa. Methods and Results MEDLINE, EMBASE, and Cochrane Library were searched, and RCTs (≥100 patients) comparing ≥2 adult cardiac surgical interventions were included. Crossover from the initial treatment assignment and relative risks (RRs) for each trial's primary end point and mortality at longest available follow-up were extracted. All RRs were calculated as >1 favored control group and <1 favored experimental arm. Primary outcome was the effect estimate for primary end point of each RCT, and secondary outcome was all-cause mortality; both were appraised as RR at the longest follow-up available. Sixty articles reporting on 47 RCTs (25 440 patients) were identified. Median crossover rate from experimental to control group was 7.0% (first quartile, 2.0%; third quartile, 9.7%), whereas from control to experimental group, the rate was 1.3% (first quartile, 0%; third quartile, 3.6%). RRs for primary end point and mortality were higher in RCTs with higher crossover rate from experimental to control group (RR, 1.01 [95% CI, 0.94-1.07] versus RR, 0.80 [95% CI, 0.66-0.97] and RR, 1.02 [95% CI, 0.95-1.11] versus RR, 0.94 [95% CI, 0.82-1.07], respectively). Crossover from control to experimental group did not alter effect estimates for primary end point or mortality (RR, 0.82 [95% CI, 0.63-1.05] versus RR, 0.95 [95% CI, 0.86-1.04] and RR, 0.88 [95% CI, 0.73-1.07] versus RR, 1.02 [95% CI, 0.95-1.09], respectively). Conclusions Crossover from experimental to control group is associated with outcomes of cardiac surgery RCTs. Crossover should be minimized at designing stage and carefully appraised after study completion.
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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 | 0.208 | 0.366 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".