Recommendations on RBC Transfusion Support in Children With Hematologic and Oncologic Diagnoses From the Pediatric Critical Care Transfusion and Anemia Expertise Initiative
Bibliographic record
Abstract
OBJECTIVES: To present the recommendations and supporting evidence for RBC transfusions in critically ill children with hematologic and oncologic disease from 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 and research priorities for RBC transfusions in critically ill children. The hematologic/oncologic subgroup included seven 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: The hematologic/oncologic subgroup developed 14 recommendations (seven clinical, seven research); all achieved greater than 80% agreement. In patients with sickle cell disease, Transfusion and Anemia Expertise Initiative recommends: 1) RBC transfusion to achieve a target hemoglobin concentration of 10 g/dL rather than hemoglobin of less than 30% prior to surgical procedures requiring general anesthesia and 2) exchange transfusion over simple (nonexchange) transfusion if the child's condition is deteriorating (based on clinical judgment), otherwise a simple, nonexchange RBC transfusion is recommended. There is insufficient evidence to make recommendations on transfusion thresholds for patients with sickle cell disease prior to minor procedures, with acute stroke or with pulmonary hypertension. For patients with oncologic disease or undergoing hematopoietic stem cell transplant, a hemoglobin concentration of 7-8 g/dL is recommended. Due to lack of evidence, research is needed to clarify the appropriate transfusion thresholds in these patients. CONCLUSIONS: Transfusion and Anemia Expertise Initiative developed specific pediatric recommendations regarding RBC transfusion management in critically ill children with sickle cell disease, oncologic disease, and hematopoietic stem cell transplant and recommendations to help guide future research priorities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".