Recommendations on RBC Transfusion in Critically Ill Children With Acute Brain Injury 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 acute brain injury 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 acute brain injury subgroup included three 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 Consensus Conference experts developed and agreed upon two clinical and two research recommendations focused on RBC transfusion in the critically ill child with acute brain injury. Recommendations include consideration of RBC transfusion for a hemoglobin concentration between 7 and 10 g/dL in patients with acute brain injury and do not support the use of brain tissue Po 2 monitoring to guide RBC transfusion decisions. Research is needed to better understand transfusion thresholds and brain tissue monitoring for pediatric patients with acute brain injury. Conclusions: The Transfusion and Anemia Expertise Initiative Consensus Conference developed pediatric-specific clinical and research recommendations regarding RBC transfusion in the critically ill child with acute brain injury. Although agreement among experts was very strong, the available pediatric evidence was extremely limited with major gaps in the literature.
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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 | 0.060 | 0.201 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".