What to Do When Your Brain Turns Blue? Considerations During Aortic Arch Surgery
Bibliographic record
Abstract
Expert Contributors Jenny Kwak, MD, is an assistant professor of anesthesiology at Loyola University Medical Center. Her interests include cardiothoracic anesthesia and resident echocardiography education. Hilary Grocott, MD, FRPC, FASE, is a professor of anesthesia and surgery at the University of Manitoba. He is also the Cardiac Anesthesia Fellowship Director at St. Boniface Hospital in Winnipeg, Manitoba. Cdr Darian Rice, MD, PhD, is the chief of cardiothoracic anesthesiology and residency program director at the Naval Medical Center, Portsmouth, Virginia. His interests include cardiothoracic and trauma anesthesiology, echocardiography, and resident education. Department of Defense disclaimer: The opinions or assertions contained herein are the private views of the author and are not to be construed as official or as reflecting the views of the Department of the Navy or the Department of Defense. David Fitzgerald, CCP, is the chief of cardiovascular perfusion at INOVA Heart and Vascular Institute and INOVA Fairfax Hospital for Children. Jeffrey Schwartz, MD, is an associate professor of thoracic and cardiovascular surgery at Loyola University Medical Center with an interest in complex aortic surgery. He is also the surgical director for the lung transplantation program and the residency program director for the cardiovascular surgery program.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".