A review and analysis of outcomes in randomized clinical trials of plasma transfusion in patients with bleeding or for the prevention of bleeding: The <scp>BEST</scp> collaborative study
Bibliographic record
Abstract
BACKGROUND: Previous systematic reviews have revealed an inconsistency of outcome definitions as a major barrier in providing evidence-based guidance for the use of plasma transfusion to prevent or treat bleeding. We reviewed and analyzed outcomes in randomized controlled trials (RCTs) to provide a methodology for describing and classifying outcomes. STUDY DESIGN AND METHODS: RCTs involving transfusion of plasma published after 2000 were identified from a prior review (Yang 2012) and combined with an updated systematic literature search of multiple databases (July 1, 2011 to January 17, 2023). Inclusion of publications, data extraction, and risk of bias assessments were performed in duplicate. (PROSPERO registration number is: CRD42020158581). RESULTS: In total, 5579 citations were identified in the new systematic search and 22 were included. Six additional trials were identified from the previous review, resulting in a total of 28 trials: 23 therapeutic and five prophylactic studies. An increasing number of studies in the setting of major bleeding such as in cardiovascular surgery and trauma were identified. Eighty-seven outcomes were reported with a mean of 11 (min-max. 4-32) per study. There was substantial variation in outcomes used with a preponderance of surrogate measures for clinical effect such as laboratory parameters and blood usage. CONCLUSION: There is an expanding literature on plasma transfusion to inform guidelines. However, considerable heterogeneity of reported outcomes constrains comparisons. A core outcome set should be developed for plasma transfusion studies. Standardization of outcomes will motivate better study design, facilitate comparison, and improve clinical relevance for future trials of plasma transfusion.
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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 | 0.070 | 0.253 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.021 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".