Plasma and Platelet Transfusion Strategies in Critically Ill Children Following Severe Trauma, Traumatic Brain Injury, and/or Intracranial Hemorrhage: From the Transfusion and Anemia EXpertise Initiative–Control/Avoidance of Bleeding
Bibliographic record
Abstract
OBJECTIVES: To present consensus statements and supporting literature for plasma and platelet transfusions in critically ill children with severe trauma, traumatic brain injury, and/or intracranial hemorrhage 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 neonates and children with severe trauma, traumatic brain injury, and/or intracranial hemorrhage. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A panel of eight experts developed expert-based statements for plasma and platelet transfusions in critically ill neonates and children with severe trauma, traumatic brain injury, and/or intracranial hemorrhage. These statements were reviewed and ratified by the 29 Transfusion and Anemia EXpertise Initiative-Control/Avoidance of Bleeding experts. A systematic review was conducted using MEDLINE, EMBASE, and Cochrane Library databases, from inception to December 2020. Consensus was obtained using the Research and Development/University of California, Los Angeles Appropriateness Method. Results were summarized using the Grading of Recommendations Assessment, Development, and Evaluation method. We developed one good practice statement and six expert consensus statements. CONCLUSIONS: The lack of evidence precludes proposing recommendations on monitoring of the coagulation system and on plasma and platelets transfusion in critically ill pediatric patients with severe trauma, severe traumatic brain injury, or nontraumatic intracranial hemorrhage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".