Recommendations on RBC Transfusions in Critically Ill Children With Acute Respiratory Failure From the Pediatric Critical Care Transfusion and Anemia Expertise Initiative
Bibliographic record
Abstract
OBJECTIVES: To present the recommendations and supporting literature for RBC transfusions in critically ill children with bleeding developed by the Pediatric Critical Care Transfusion and Anemia Expertise Initiative. DESIGN: Consensus conference series of international, multidisciplinary experts in RBC transfusion management of critically ill children. METHODS: The panel of 38 experts developed evidence-based and, when evidence was lacking, expert-based clinical recommendations as well as research priorities for RBC transfusions in critically ill children. The respiratory subgroup included six experts. Electronic searches were conducted using PubMed, EMBASE, and Cochrane Library databases from 1980 to May 2017. Agreement was obtained using the Research and Development/UCLA Appropriateness Method. Results were summarized using the Grading of Recommendations Assessment, Development, and Evaluation method. RESULTS: Transfusion and Anemia Expertise Initiative experts developed seven recommendations focused on children with acute respiratory failure. All recommendations reached agreement (> 80%). Transfusion of RBCs in children with respiratory failure with an hemoglobin level less than 5 g/dL was strongly recommended. It was strongly recommended that RBCs not be systematically administered to children with respiratory failure who are hemodynamically stable and who have a hemoglobin level greater than or equal to 7 g/dL. Experts could not make a recommendation for children with hemodynamic instability, with severe hypoxemia and/or with an hemoglobin level between 5 and 7 g/dL. Specific RBC transfusion strategies using physiologic-based metrics and biomarkers could not be elaborated. CONCLUSIONS: The Transfusion and Anemia Expertise Initiative Consensus Conference developed specific recommendations regarding RBC transfusion management in critically ill children with respiratory failure, as well as recommendations to guide future research. Clinical recommendations emphasize relevant hemoglobin thresholds. Research recommendations emphasize the need to identify appropriate physiologic thresholds, suggest a better understanding of alternatives to RBC transfusion, and identify the need for better evidence on hemoglobin thresholds that might be used in specific subpopulations of critically ill children.
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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 | 0.053 | 0.172 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".