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Record W4205121403 · doi:10.1097/pcc.0000000000002851

Executive Summary of Recommendations and Expert Consensus for Plasma and Platelet Transfusion Practice in Critically Ill Children: From the Transfusion and Anemia EXpertise Initiative—Control/Avoidance of Bleeding (TAXI-CAB)

2022· review· en· W4205121403 on OpenAlexaff
Marianne E. Nellis, Oliver Karam, Stacey L. Valentine, Scot T. Bateman, Kenneth E. Remy, Jacques Lacroix, Jill M. Cholette, Melania M. Bembea, Robert T. Russell, Marie E. Steiner, Susan M. Goobie, Marisa Tucci, Paul A. Stricker, Simon Stanworth, Meghan Delaney, Lani Lieberman, Jennifer A. Muszynski, David F. Bauer, Katherine Steffen, Daniel K. Nishijima, Juan C. Ibla, Sitaram M. Emani, Adam M. Vogel, Thorsten Haas, Ruchika Goel, Gemma Crighton, Diana Delgado, Michelle Demetres, Robert I. Parker

Bibliographic record

VenuePediatric Critical Care Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsUniversity of TorontoUniversity Health NetworkCentre Hospitalier Universitaire Sainte-Justine
FundersNational Center for Advancing Translational SciencesNational Institute of Neurological Disorders and StrokeNational Heart, Lung, and Blood Institute
KeywordsMedicineExtracorporeal membrane oxygenationIntensive care medicinePlatelet transfusionCochrane LibraryFresh frozen plasmaSurgeryPlateletMeta-analysisInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Critically ill children frequently receive plasma and platelet transfusions. We sought to determine evidence-based recommendations, and when evidence was insufficient, we developed expert-based consensus statements about decision-making for plasma and platelet transfusions in critically ill pediatric patients. DESIGN: Systematic review and consensus conference series involving multidisciplinary international experts in hemostasis, and plasma/platelet transfusion in critically ill infants and children (Transfusion and Anemia EXpertise Initiative-Control/Avoidance of Bleeding [TAXI-CAB]). SETTING: Not applicable. PATIENTS: Children admitted to a PICU at risk of bleeding and receipt of plasma and/or platelet transfusions. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A panel of 29 experts in methodology, transfusion, and implementation science from five countries and nine pediatric subspecialties completed a systematic review and participated in a virtual consensus conference series to develop recommendations. The search included MEDLINE, EMBASE, and Cochrane Library databases, from inception to December 2020, using a combination of subject heading terms and text words for concepts of plasma and platelet transfusion in critically ill children. Four graded recommendations and 49 consensus expert statements were developed using modified Research and Development/UCLA and Grading of Recommendations, Assessment, Development, and Evaluation methodology. We focused on eight subpopulations of critical illness (1, severe trauma, intracranial hemorrhage, or traumatic brain injury; 2, cardiopulmonary bypass surgery; 3, extracorporeal membrane oxygenation; 4, oncologic diagnosis or hematopoietic stem cell transplantation; 5, acute liver failure or liver transplantation; 6, noncardiac surgery; 7, invasive procedures outside the operating room; 8, sepsis and/or disseminated intravascular coagulation) as well as laboratory assays and selection/processing of plasma and platelet components. In total, we came to consensus on four recommendations, five good practice statements, and 44 consensus-based statements. These results were further developed into consensus-based clinical decision trees for plasma and platelet transfusion in critically ill pediatric patients. CONCLUSIONS: The TAXI-CAB program provides expert-based consensus for pediatric intensivists for the administration of plasma and/or platelet transfusions in critically ill pediatric patients. There is a pressing need for primary research to provide more evidence to guide practitioners.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.362
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations78
Published2022
Admission routes1
Has abstractyes

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