Randomized controlled trials of aprotinin in cardiac surgery: could clinical equipoise have stopped the bleeding?
Bibliographic record
Abstract
BACKGROUND: Aprotinin is a serine protease inhibitor used to limit perioperative bleeding and reduce the need for donated blood transfusions during cardiac surgery. Randomized controlled trials of aprotinin evaluating its effect on the outcome of perioperative transfusion have been published since 1987, and systematic reviews were conducted in 1992 and 1997. METHODS: A systematic search was conducted for all RCTs of aprotinin that used placebo controls or were open-label with no active control treatment. Data collected included the primary outcome, objective of each study, whether a systematic review was cited or conducted as part of the background and/or rationale for the study and the number of previously published RCTs cited. Cumulative meta-analyses were performed. RESULTS: Sixty-four randomized, controlled trials of aprotinin were found, conducted between 1987 and 2002, reporting an endpoint of perioperative transfusion. Median trial size was 64 subjects, with a range of 20 to 1784. A cumulative meta-analysis indicated that aprotinin greatly decreased the need for perioperative transfusion, stabilizing at an odds ratio of 0.25 (p < 10 - 6) by the 12th study, published in June of 1992. The upper limit of the confidence interval never exceeded 0.65 and results were similar in all subgroups. Citation of previous RCTs was extremely low, with a median of 20% of prior trials cited. Only 7 of 44 (15%) of subsequent reports referenced the largest trial (N = 1784), which was 28 times larger than the median trial size. CONCLUSIONS: This study demonstrates that investigators evaluating aprotinin were not adequately citing previous research, resulting in a large number of RCTs being conducted to address efficacy questions that prior trials had already definitively answered. Institutional review boards and journals could reduce the number of redundant trials by requiring investigators to conduct adequate searches for prior evidence and conducting systematic reviews.
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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 | 0.162 | 0.368 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".