The Influence of the Extracorporeal Membrane Oxygenation Circuit and Components on Anticoagulation Management: The Pediatric Extracorporeal Membrane Oxygenation Anticoagulation CollaborativE Consensus Conference
Bibliographic record
Abstract
OBJECTIVES: To derive systematic-review informed, modified Delphi consensus regarding the influence of extracorporeal membrane oxygenation (ECMO) circuit components on anticoagulation practices for pediatric ECMO for the Pediatric ECMO Anticoagulation CollaborativE. DATA SOURCES: A structured literature search was performed using PubMed, EMBASE, and Cochrane Library (CENTRAL) databases from January 1988 to May 2021. STUDY SELECTION: Management of ECMO anticoagulation in the setting of different ECMO circuit components. DATA EXTRACTION: Two authors reviewed all citations independently, with a third independent reviewer resolving conflicts. Twenty-nine references were used for data extraction and informed recommendations, evidence-based consensus statements, and good practice statements. 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-based recommendations and, when evidence was lacking, expert-based consensus statements or good practice statements for the influence of ECMO circuit and components on anticoagulation management. A web-based modified Delphi process was used to build consensus via the Research And Development/University of California Appropriateness Method. Consensus was defined as greater than 80% agreement. One good practice statement, 2 weak recommendations, and 2 consensus statements are presented. CONCLUSIONS: The incorporation of new component technologies into clinical practice has outpaced clinical investigations of anticoagulation strategies for pediatric ECMO. Future investigations should leverage academic and industrial collaborations, translational platforms, and modern biostatistical methods to improve patient outcomes.
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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 | 0.291 | 0.489 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".