The Adequacy of Basic Intraoperative Transesophageal Echocardiography Performed by Experienced Anesthesiologists
Bibliographic record
Abstract
UNLABELLED: Transesophageal echocardiography (TEE) may improve intraoperative decision-making and patient outcome if it is performed and interpreted correctly. After revising our TEE examination to fulfill the published guidelines for basic TEE practitioners, we prospectively evaluated the ability of our cardiac anesthesiologists (all very experienced with TEE) to record and interpret this revised examination. Educational aids and regular TEE performance feedback were provided to the anesthesiologists. Their interpretations were compared with the independently determined results of experts. Compared with their own historical controls (42% recording rate), all anesthesiologists showed significant improvement in their ability to record a basic intraoperative TEE examination resulting in 81% (P < 0.0001) of all required images being recorded: 88% before cardiopulmonary bypass, 77% immediately after bypass, and 64% after chest closure. Seventy-nine percent of the images recorded at baseline were correctly interpreted, 6% were incorrectly interpreted, and 15% were not evaluated. Our attempt to assess compliance with published guidelines for basic intraoperative TEE resulted in a marked improvement in our intraoperative TEE practice. Most, but not all, standard cross-sections are recorded or interpreted correctly, even by highly experienced and motivated practitioners. IMPLICATIONS: Experienced cardiac anesthesiologists can obtain and correctly interpret most basic intraoperative transesophageal echocardiograms.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".