Resuscitative transesophageal echocardiography
Bibliographic record
Abstract
Resuscitative transesophageal echocardiography (rTEE) has emerged as a transformative point-of-care imaging modality that integrates diagnostic and procedural guidance into real-time resuscitation. Unlike transthoracic echocardiography (TTE), rTEE provides continuous, high-resolution cardiac imaging without interrupting chest compressions, overcoming traditional limitations in patients with undifferentiated shock or cardiac arrest. This review summarizes the evolution, technical foundations, and clinical applications of rTEE across resuscitation, extracorporeal membrane oxygenation (ECMO), and peri-arrest care. We discuss the development of focused scanning protocols—such as the ACEP 3-view, Resuscitative TEE 4-view, and 3 + 2 frameworks—that enable rapid qualitative assessment of cardiac activity, ventricular function, volume status, and reversible causes of arrest. Diagnostic advantages include superior rhythm classification (distinguishing pulseless electrical activity (PEA) , pseudo-PEA, fine VF, and standstill), improved pulse-check accuracy, and identification of the area of maximal compression (AMC) to optimize CPR quality. Procedurally, rTEE supports real-time ECMO cannulation, monitoring, and decannulation, complementing ELSO recommendations for both V-A and V–V configurations. Evidence-based echocardiographic parameters—such as LVOT velocity time integral (VTI), MAPSE, TAPSE, and t-IVT—inform readiness for ECMO liberation and predict recovery or need for durable mechanical support. Focused rTEE training pathways and credentialing frameworks are now available for anesthesiologists, intensivists, and emergency physicians, expanding its accessibility in perioperative and critical care environments. As the technology becomes more widespread, future research should standardize rTEE competency assessment, validate outcome-based protocols, and further integrate rTEE into precision-guided resuscitation algorithms.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".