Intraoperative transesophageal echocardiography following mitral valve repair: a systematic review
Bibliographic record
Abstract
OBJECTIVE: We aimed to examine the recent evidence and search for novel assessments on intraoperative TEE following mitral valve repair that can impact short and long-term outcomes. METHODS: The Ovid MEDLINE, PubMed, and EMBASE databases were searched from January 1, 2008, until January 27, 2021, for studies on patients with severe Mitral Valve Regurgitation (MR) undergoing Mitral Valve (MV) repair surgery with intraoperative Transesophageal Echocardiography (TEE) performed after the repair. Additional searches were conducted using Google search engine, Web of Science, and Cochrane Library. RESULTS: After reviewing 302 records, 8 retrospective and 22 prospective studies were included (n = 30). Due to clinical and methodological diversity, these studies are noncomparable and data were not amenable to quantitative synthesis. CONCLUSION: Although technological advances allowed the objective assessment of geometric and dynamic alterations of the MV, the impact of the use of these technologies on short- or long-term outcomes was not studied. There is uncertainty and conflicting evidence on the ideal method and metrics to evaluate MV patency post-repair. Few isolated studies validated methods to assess coaptation surface and LV function post-repair.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".