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Record W2339056622 · doi:10.1097/mcc.0000000000000309

Monitoring during extracorporeal membrane oxygenation

2016· review· en· W2339056622 on OpenAlexaff
Ghislaine Douflé, Niall D. Ferguson

Bibliographic record

VenueCurrent Opinion in Critical Care · 2016
Typereview
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of TorontoMount Sinai HospitalUniversity Health NetworkToronto General Hospital
Fundersnot available
KeywordsExtracorporeal membrane oxygenationMedicineIntensive care medicineExtracorporealRespiratory failureSurgery

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: An increasing number of patients are placed on extracorporeal membrane oxygenation (ECMO) for respiratory or cardiac failure. Sound understanding of physiology and configuration of ECMO is essential for proper management. This review covers different monitoring parameters and tools for patients supported with different types of ECMO. RECENT FINDINGS: Emphasis is placed on monitoring saturations at different sites depending on type of ECMO support. The main monitoring tools detailed in this review are echocardiography and pulmonary artery catheters. SUMMARY: The review will help physicians better assess adequate ECMO support by using the appropriate parameters for each type of configuration.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.186
GPT teacher head0.431
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations37
Published2016
Admission routes1
Has abstractyes

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