Impact of Critical Care Transesophageal Echocardiography in Medical–Surgical ICU Patients: Characteristics and Results From 274 Consecutive Examinations
Bibliographic record
Abstract
OBJECTIVE: Critical care echocardiography has become an integral tool in the assessment and management of critically ill patients. Critical care transesophageal echocardiography (TEE) offers diagnostic reliability, superior image quality, and an expanded diagnostic scope to transthoracic echocardiography. Despite its favorable attributes, TEE use in North American intensive care units (ICUs) remains relatively undescribed. In this article, we seek to characterize the feasibility, indications, and clinical impact of a critical care TEE program. DESIGN: Retrospective, observational study. SETTING: Tertiary care, academic critical care program consisting of 2 hospitals in Ontario, Canada. PARTICIPANTS: Consecutive critical care TEE examinations on ICU patients performed between December 2012 and December 2016. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Consecutive critical care TEE studies on ICU patients from December 1, 2012, to December 31, 2016, were reviewed. The TEEs performed on cardiac surgery patients and those without reports were excluded. Examination details, including indications, complications, examination complexity (number of views, Doppler techniques), and clinical recommendations were aggregated and analyzed. Two hundred seventy-four TEE studies were performed by 38 operators. Common indications for TEE studies were hemodynamic instability (45.2%), assessment for infective endocarditis (22.2%), and cardiac arrest (20.1%). A change in patient management was proposed following 79.5% of TEE studies. Thirty-eight percent of TEE studies were performed during evening hours or on weekends. There were no mechanical complications. CONCLUSIONS: Our observational data support intensivist-performed TEE as being safe and therapeutically influential across a broad range of indications. Our program's demonstrated feasibility and impact may act as a model for TEE adoption in other North American ICUs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".