Influence of transesophageal echocardiography on intraoperative decision making for toronto stentless prosthetic valve implantation.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: Intraoperative transesophageal echocardiography (TEE) is commonly used during aortic valve surgery. In aortic valve replacement (AVR), this permits measurement of the aortic annulus, study of the anatomy of aortic valve components, and prediction of prosthesis valve size. After cardiopulmonary bypass (CPB), echocardiography is valuable in checking prosthesis function. In this study, we evaluated the impact of intraoperative TEE on the decision-making process of aortic Toronto stentless prosthetic valve (TSPV) implantation. METHODS: Fifty-two consecutive patients undergoing elective AVR were collected prospectively. Multiplane TEE was performed before CPB to determine diameters of the aortic valve annulus and sinotubular junction. This was to evaluate the feasibility of TSPV implantation in the aortic position and to predict prosthesis size. Further TEE evaluation was carried out after CPB to assess prosthetic valve function. RESULTS: TEE allowed measurement of the aortic annulus and sinotubular junction, and enabled correct prediction of prosthesis size. Ultrasonic evaluation also revealed contraindications to TSPV implantation in five patients. In one case, color-Doppler examination led to immediate successful surgical correction of prosthetic incompetence. CONCLUSION: Intraoperative multiplane TEE examination is useful in the decision-making process in AVR with the TSPV by selecting patients suitable for the stentless valve, predicting prosthesis size, and checking prosthesis function.
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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 | 0.016 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".