Intraoperative Echocardiography: Support for Decision Making in Cardiac Surgery
Bibliographic record
Abstract
Intraoperative echocardiography (including transesophageal echocardiography, epiaortic ultrasound and epicardial echocardiography) is commonly performed in North American hospitals during cardiac anesthesia. Several authors have reported on the positive impact of intraoperative echocardiography on patients' outcomes. Transesophageal echocardiography is useful in identifying anatomic and functional abnormalities either before or after cardiopulmonary bypass and helps to make decisions in the care of high-risk and unstable patients. In minimally invasive and robotically assisted surgery, transesophageal echocardiography is essential in order to guide cannulation of venous and arterial vessels for cardiopulmonary bypass and in providing immediate assessment of the quality of the performed repair. Intraoperative echocardiography can also detect complications associated with the performed procedure and can be an excellent hemodynamic monitor in unstable patients. In this paper different scenarios where intraoperative echocardiography is useful are reviewed, some clinical cases are shown to illustrate, and a review of related literature is reported.
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 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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".