Anatomic suitability for transapical transcatheter mitral valve implantation using a tether‐based device
Bibliographic record
Abstract
BACKGROUND: Transcatheter mitral valve implantation (TMVI) is a novel therapeutic option for treating symptomatic mitral valve disease. Evaluating patient anatomical suitability is a critical step in the TMVI screening process, but currently requires specialized software and computerized device models. AIMS: This analysis sought to assess the effectiveness of simple and standardized multislice computed tomography (MSCT) anatomic measurements for their ability to discriminate between patients who passed anatomical screening for Tendyne™ TMVI. METHODS: Subjects screened for the Tendyne Expanded Clinical Study from January 2016 through September 2019 were included. Core laboratory screening measurements included mitral annular (MA) dimensions at end-systole and end-diastole, simulated device implantation, and neo-left ventricular outflow tract (LVOT) area. Additionally, nine standard measurements of patient anatomy were assessed for their predictive value of patients passing the anatomic screening process. RESULTS: Out of 496 subjects screened for eligibility, 257 subjects met clinical eligibility criteria with MA dimensions within the manufacturer's suggested range: 153 (59.5%) underwent TMVI while 104 (40.5%) were excluded from the study for other anatomic reasons (76% due to risk of LVOT obstruction). CT-derived left ventricular end-systole diameter (LVESD) had the highest discriminatory power for predicting TMVI anatomical suitability (area under the curve of 0.908, p < 0.0001). The mitral inter-commissural (IC) dimension was best predictive of annular dimensions being within range, with dimensions <30 or >50 mm resulting in a negative predictive value of 94.4%. CONCLUSIONS: MSCT-derived mitral IC dimension and LVESD easily performed measures that are effective predictors of anatomical suitability or screen failure for this tether-based TMVI device.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".