Lifetime management considerations to optimise transcatheter aortic valve implantation: a practical guide
Bibliographic record
Abstract
Transcatheter aortic valve implantation (TAVI) is a safe and effective procedure for the treatment of aortic stenosis. With the recently broadened indications, there is a larger cohort of patients likely to outlive their first transcatheter heart valve (THV). This review discusses relevant lifetime planning considerations, focusing on the utility of preprocedural computed tomography imaging to help implanters future-proof their patients who are likely to outlive their first valve. The initial priority is to optimise the index procedure by maximising THV haemodynamic function and durability. This involves maximising the effective orifice area, minimising the risk of new pacemaker implantation, reducing paravalvular regurgitation, and preventing coronary obstruction and annular rupture. In patients requiring a second valve procedure, a significant proportion will require a TAVI-in-TAVI, and implanters should consider the key priorities for a redo procedure, including the increased risks of patient-prosthesis mismatch and conduction abnormalities, promoting coronary reaccessibility, and preventing coronary obstruction and sinus sequestration. Careful planning can identify potential hurdles as well as predict the feasibility and likely outcomes of redo-TAVI, to help individualise care over the lifetime of each patient.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".