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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".