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Record W4281491988 · doi:10.1097/pcc.0000000000002991

Extracorporeal Membrane Oxygenation Candidacy Decisions: An Argument for a Process-Based Longitudinal Approach*

2022· article· en· W4281491988 on OpenAlexaff
Katie M. Moynihan, Melanie Jansen, Bryan D. Siegel, Lisa S. Taylor, Roxanne Kirsch

Bibliographic record

VenuePediatric Critical Care Medicine · 2022
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsCandidacyExtracorporeal membrane oxygenationMedicineAppealIntensive care medicineArgument (complex analysis)AccountabilityTransparency (behavior)Process (computing)Medical emergencySurgery

Abstract

fetched live from OpenAlex

Are all children extracorporeal membrane oxygenation (ECMO) candidates? Navigating ECMO decisions represents an enormous challenge in pediatric critical care. ECMO cannulation should not be a default option as it will not confer benefit for "all" critically ill children; however, "all" children deserve well-considered decisions surrounding their ECMO candidacy. The complexity of the decision demands a systematic, "well-reasoned" and "dynamic" approach. Due to clinical urgency, this standard cannot always be met prior to initiation of ECMO. We challenge the paradigm of "candidacy" as a singular decision that must be defined prior to ECMO initiation. Rather, the determination as to whether ECMO is in the patient's best interest is applicable regardless of cannulation status. The priority should be on collaborative, interdisciplinary decision-making processes aligned with principles of transparency, relevant reasoning, accountability, review, and appeal. To ensure a robust process, it should not be temporally constrained by cannulation status. We advocate that this approach will decrease both the risk of not initiating ECMO in a patient who will benefit and the risk of prolonged, nonbeneficial support. We conclude that to ensure fair decisions are made in a patient's best interest, organizations should develop procedurally fair processes for ECMO decision-making that are not tied to a particular time point and are revisited along the management trajectory.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.446
Teacher spread0.353 · 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
GenreEmpirical

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

Citations22
Published2022
Admission routes1
Has abstractyes

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