Intraoperative transesophageal echocardiogram evaluation for liver transplantation
Bibliographic record
Abstract
Transesophageal echocardiography (TEE) is a powerful tool for diagnosis and management in both cardiac and noncardiac surgeries.TEE is especially relevant during liver transplantation as patients often have underlying cardiac or pulmonary diseases, large volume shifts are anticipated, and patients are prone to thrombotic complications.The American Society of Echocardiography Guidelines assigned a grade B2 to TEE as a hemodynamic monitoring tool in this context [1].We present a TEE protocol developed at London Health Science Centre for use during liver transplantation (Table 1) based on recent literature [2][3][4][5].This protocol includes a comprehensive baseline examination during the dissection phase, followed by a more focused assessment during the anhepatic and neohepatic phases.We emphasize the importance of labeling the TEE loops during the different phases for quality of reporting, as well as for education and research purposes.During the anhepatic phase, we limit TEE probe manipulation and focus predominantly on midesophageal views.During the neohepatic phase, we aim for windows through the inferior vena cava and perform hepatic vein evaluation if the quality of the images permits.Although these views are not yet standardized in liver transplantation, there is increasing interest in the literature in this regard, and we try to include them as part of our global evaluation.We hope to see more literature regarding TEE during liver transplantation and the development of focused guidelines for use of TEE as a perioperative tool for the transplant anesthesiologist.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".