Transesophageal echocardiography: what the anesthesiologist has to know.
Bibliographic record
Abstract
Transesophageal echocardiography (TEE) is a very powerful intraoperative monitoring tool. It allows precise assessment of cardiac anatomy together with dynamic quantification of myocardial performance and flows through the heart chambers. With a high safety profile TEE counts few absolute contraindications. Performance of TEE requires dedicated training. Certification pathways are offered in Europe and North America. Focused, basic and advanced scanning protocols have been lately described for intraoperative and emergent use. Many specific TEE applications have been described in non-cardiac surgery most of which only supported by a limited number of studies. Advanced TEE allows quantitative assessment of left and right ventricular function but its application has not become standard of care outside the cardiac operative room partially due to lack of scientific evidence. TEE can provide quantification of left ad right ventricular cardiac output and diastolic function. It may also identify fluid responsiveness. TEE is more sensitive than ECG in identifying myocardial ischemia but it requires advanced training. Basic TEE can identify common causes of hemodynamic instability such as hypovolemia, pulmonary embolism and tamponade. Unexplained hemodynamic instability is the only strong indication in non-cardiac surgery. Qualitative assessment based on a simplified protocol seams to adequately address the clinical needs in this specific scenario. More studies are required to support the use of TEE outside of cardiac surgery at its full potential.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".