Echocardiographic measurements alone do not provide accurate non-invasive selection of annuloplasty band size for robotic mitral valve repair.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: Successful mitral valve repair (MVP) is dependent on accurate annuloplasty band sizing. This is difficult and time-consuming when performed via port-access, or through a 4-cm minithoracotomy used in robotically assisted MVP. With the goal of moving toward a less-invasive approach and minimizing cross-clamp time, an attempt was made to determine annuloplasty band size using transesophageal echocardiography (TEE) alone. METHODS: The intertrigonal distance (ITD) was determined by dividing the left ventricular outflow tract diameter (LVOT: measured on standard midesophageal aortic valve long-axis view) by 0.8. The ITD was compared to a nomogram developed to select the best Cosgrove-Edwards annuloplasty band size. RESULTS: Between July and October, 2004, 11 patients (mean age 52.6 +/- 17.9 years; four Barlow's valves with bileaflet prolapse, four posterior leaflet prolapses, one anterior leaflet prolapse, one rheumatic, one dilated annulus) undergoing robotically assisted MVP had the annuloplasty band chosen using TEE alone. Seven patients (63.6%) had no or mild mitral regurgitation (MR) on postoperative TEE. Three patients (27.2%) had some systolic anterior motion (SAM), with one (Barlow's valve) requiring a second repair (same operation). One patient (9.1%, rheumatic) had grade 2+ MR on postoperative TEE. CONCLUSION: In this small case series, a substantial proportion of patients had suboptimal immediate postoperative results. This suggests that selection of the annuloplasty band should not be based on a single echocardiographic variable as it depends on the etiology of the MR, and other dimensions of the mitral valve. Further studies are ongoing to develop a non-invasive method for the selection of annuloplasty band size.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".