Anticoagulant Medications: The Pediatric Extracorporeal Membrane Oxygenation Anticoagulation CollaborativE Consensus Conference
Bibliographic record
Abstract
OBJECTIVES: To derive systematic-review informed, modified Delphi consensus regarding the medications used for anticoagulation for pediatric extracorporeal membrane oxygenation (ECMO) for the Pediatric ECMO Anticoagulation CollaborativE (PEACE). DATA SOURCES: A structured literature search was performed using PubMed, EMBASE, and Cochrane Library (CENTRAL) databases from January 1988 to May 2021. STUDY SELECTION: Included studies assessed anticoagulation used in pediatric ECMO. DATA EXTRACTION: Two authors reviewed all citations independently, with a third reviewer adjudicating any conflicts. Eighteen references were used for data extraction as well as for creation of recommendations. Evidence tables were constructed using a standardized data extraction form. DATA SYNTHESIS: Risk of bias was assessed using the Quality in Prognosis Studies tool. The evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation system. Forty-eight experts met over 2 years to develop evidence-informed recommendations and, when evidence was lacking, expert-based consensus statements, or good practice statements for anticoagulation during pediatric ECMO. A web-based modified Delphi process was used to build consensus via the Research and Development/University of California Appropriateness Method. Consensus was based on a modified Delphi process with agreement defined as greater than 80%. Two recommendations, two consensus statements, and one good practice statement were developed, and, in all, agreement greater than 80% was reached. CONCLUSIONS: There is insufficient evidence to formulate optimal anticoagulation therapy during pediatric ECMO. Additional high-quality research is needed to inform evidence-based practice for anticoagulation during pediatric ECMO.
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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 | 0.225 | 0.318 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.011 | 0.007 |
| 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".