Pre-interventional assessment and calcification score of the aortic valve and annulus, with multi-detector CT, in transcatheter aortic valve implantation (TAVI) using the Medtronic CoreValve
Why this work is in the frame
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Bibliographic record
Abstract
Background: Transcatheter aortic valve implantation (TAVI) provides an acceptable alternative for aortic valve replacement in the elderly, but needs accurate pre-procedural imaging to optimise intervention. Objectives: To evaluate an alternative manual aortic valve calcification scoring system with computed tomography, for patients undergoing TAVI. We hypothesise a correlation between the Free State aortic valve calcium computed tomography score (FACTS) scoring system, valve plaque density and procedure-related complications. Methods: Twenty patients suitable for TAVI were selected according to standard international guidelines and received multimodality imaging prior to intervention. Images were reviewed by two reviewers who were blinded to each other’s scores. Where large inter-individual score variations existed, retraining was done and scores repeated, using a double-blinded method. Matched scores were included in the final analysis. Rosenhek calcification scores were used as a standard of reference. Results: The study comprised 9 (45%) men and 11 (55%) women, with a median age of 83.5 years. Median EuroSCORE was 15.5. FACTS scores ≥6 were associated with the presence of a paravalvular leak (p = 0.01). Procedure-related complications (left bundle branch block, repositioning of the valve and anaemia) were seen in patients with plaques measuring ≥1000 HU (p = 0.07). Conclusion: The FACTS score and averaged valve plaque HU showed potential for predicting a paravalvular leak and procedure-related complications, and could be valuable in the future for optimising patient selection for TAVI.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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 it