Monitoring during extracorporeal membrane oxygenation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Extracorporeal membrane oxygenation (ECMO) offers advanced mechanical support to patients with severe acute respiratory and/or cardiac failure. Ensuring an adequate therapeutic approach as well as prevention of ECMO-associated complications, by means of timely liberation, forms an essential part of standard ECMO care and is only achievable through continuous monitoring and evaluation. This review focus on the cardiorespiratory monitoring tools that can be used to assess and titrate adequacy of ECMO therapy; as well as methods to assess readiness to wean and/or discontinue ECMO support. RECENT FINDINGS: Surrogates of tissue perfusion and near infrared spectroscopy are not standards of care but may provide useful information in select patients. Echocardiography allows to determine cannulas position, evaluate cardiac structures, and function, and diagnose complications. Respiratory monitoring is mandatory to achieve lung protective ventilation and identify early lung recovery, surrogate measurements of respiratory effort and ECMO derived parameters are invaluable in optimally managing ECMO patients. SUMMARY: Novel applications of existing monitoring modalities alongside evolving technological advances enable the advanced monitoring required for safe delivery of ECMO. Liberation trials are necessary to minimize time sensitive ECMO related complications; however, these have yet to be standardized.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".