Bibliographic record
Abstract
J. Michael Brewer, DO, MS, is a cardiovascular and ECMO intensivist at INTEGRIS Health Baptist Medical Center, Oklahoma City, Oklahoma, USA. He is also affiliated with the Program in Health Quality at Queen’s University, Kingston, Ontario, Canada. His clinical expertise includes ECMO transport and venopulmonary ECMO, topics on which he has published numerous manuscripts. He also has a keen interest and expertise in healthcare quality, specifically in using teamwork interventions to improve the performance and effectiveness of multidisciplinary ECMO teams.Marc O. Maybauer, MD, PhD, EDIC, FCCP, FACC, FASE, is Professor and Chief of Critical Care Medicine, Executive Director of the Critical Care Organization, and Director of the Anesthesiology ECMO Program at the University of Florida, Gainesville, Florida, USA. In addition, he holds professorships with the University of Queensland, Australia, and the Philipps University of Marburg, Germany. Dr. Maybauer is the Editor of the textbook Extracorporeal Membrane Oxygenation - A Problem Based Learning Approach with Oxford University Press. He has a special interest in Veno-Pulmonary (VP) ECMO for acute right ventricular support. Dr. Maybauer has described several innovative VP ECMO configurations and is further investigating this novel ECMO mode.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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; both teacher heads agree on what is shown here.
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".