Research Priorities for Plasma and Platelet Transfusion Strategies in Critically Ill Children: From the Transfusion and Anemia EXpertise Initiative–Control/Avoidance of Bleeding
Bibliographic record
Abstract
OBJECTIVES: To present a list of high-priority research initiatives for the study of plasma and platelet transfusions in critically ill children from the Transfusion and Anemia EXpertise Initiative-Control/Avoidance of Bleeding. DESIGN: Systematic review and consensus conference of international, multidisciplinary experts in platelet and plasma transfusion management of critically ill children. SETTING: Not applicable. PATIENTS: Critically ill pediatric patients at risk of bleeding and receiving plasma and/or platelet transfusions. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A panel of 13 experts developed research priorities for the study of plasma and platelet transfusions in critically ill children which were reviewed and ratified by the 29 Transfusion and Anemia EXpertise Initiative-Control/Avoidance of Bleeding experts. The specific priorities focused on the following subpopulations: severe trauma, traumatic brain injury, intracranial hemorrhage, cardiopulmonary bypass surgery, extracorporeal membrane oxygenation, oncologic diagnosis or stem cell transplantation, acute liver failure and/or liver transplantation, noncardiac surgery, invasive procedures outside of the operating room, and sepsis and/or disseminated intravascular coagulation. In addition, tests to guide plasma and platelet transfusion, as well as component selection and processing, were addressed. We developed four general overarching themes and 14 specific research priorities using modified Research and Development/University of California, Los Angeles methodology. CONCLUSIONS: Studies are needed to focus on the efficacy/harm, dosing, timing, and outcomes of critically ill children who receive plasma and/or platelet transfusions. The completion of these studies will facilitate the development of evidence-based recommendations.
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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 | 0.220 | 0.263 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".