Abstract 273: Development and Implementation of a Multicenter Registry for Resuscitation-Focused Transesophageal Echocardiography
Bibliographic record
Abstract
Objectives: To evaluate the clinical impact, safety, and clinical outcomes of focused transesophageal echocardiography (TEE) in the evaluation of critically ill patients in the emergency department (ED) and intensive care units (ICU). Methods: We established a prospective, multicenter, observational registry involving adult critically ill patients in whom focused TEE was performed for evaluation of out-of-hospital cardiac arrest (OHCA), in-hospital cardiac arrest (IHCA), evaluation of undifferentiated shock, hemodynamic monitoring, and/or procedural guidance in the ED, ICU, or operating room (OR) setting. The primary objective of the current investigation was to evaluate the clinical impact and safety of focused, point-of-care TEE in critically ill patients. Data elements included patient and procedure characteristics, laboratory values, timing of interventions, clinical outcomes, and TEE video images. Results: A total of 771 cases were collected from 27 hospitals, including 506 (66%) intra and post arrest OHCA and IHCA, 221 (29%) initial evaluation of undifferentiated shock, 71 (9%) hemodynamic monitoring, and 93 (12%) procedural guidance. TEE changed management in 64% of cases of OHCA, in 71% of IHCA, and in 85% of patients with undifferentiated shock. There were no reported esophageal perforations or oropharyngeal injuries, and other procedural complications were rare. Conclusion: A prospective, multicenter, and multidisciplinary TEE registry was successfully implemented, and demonstrated that focused TEE is safe and clinically impactful across multiple critical care applications. Further studies from this research network will accelerate the development of outcome-oriented research and knowledge translation on the use of TEE in emergency and critical care settings.
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".