A bench test study of bioprosthetic valve fracture performed before versus after transcatheter valve-in-valve intervention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: Bioprosthetic valve fracture (BVF) may improve transvalvular gradients and transcatheter heart valve (THV) expansion during VIV interventions. However, the optimal timing of BVF is unknown. We assessed the impact of timing of BVF (before versus after) for valve-in-valve (VIV) intervention, on hydrodynamic function and THV expansion. METHODS AND RESULTS: Three THV designs were assessed, a 23 mm SAPIEN 3 (S3), small ACURATE neo (ACn) and 23 mm Evolut R, deployed into 21 mm Mitroflow bioprosthetic surgical valves. We evaluated each THV in three groups: 1) no BVF, 2) BVF before VIV, and 3) BVF after VIV. Hydrodynamic testing was performed using a pulse duplicator to ISO 5840:2013 standard. Transvalvular gradients were lower when BVF was performed after VIV for the S3 (no BVF 15.5 mmHg, BVF before VIV 8.0 mmHg, BVF after VIV 5.6 mmHg), and the ACn (no BVF 9.8 mmHg, BVF before VIV 8.4 mmHg, BVF after VIV 5.1 mmHg). Transvalvular gradients were similar for the Evolut R, irrespective of performance of BVF or timing of BVF. BVF performed after VIV resulted in better expansion in all three THV designs. The ACn and Evolut R samples all had a mild degree of pinwheeling, and BVF timing did not impact on pinwheeling severity. The S3 samples had severe pinwheeling with no BVF, and significant improvement in pinwheeling when BVF was performed after VIV. CONCLUSIONS: BVF performed after VIV was associated with superior THV expansion in all three THV designs tested, with lower residual transvalvular gradients in the S3 and ACn THVs. The Evolut R had similar hydrodynamic performance irrespective of BVF timing. Timing of BVF has potential implications on THV function.
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.
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 | 0.006 |
| 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) | 0.001 | 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 it