Impact of implant depth on hydrodynamic function of the ALLEGRA bioprosthesis in valve-in-valve interventions
Bibliographic record
Abstract
AIMS: We aimed to assess the impact of implant depth on hydrodynamic function following valve-in-valve (VIV) intervention using the ALLEGRA transcatheter heart valve (THV) in three different surgical valve designs. METHODS AND RESULTS: Multiple implantation depths (+2 mm, -2 mm and -6 mm) were tested using a 23 mm ALLEGRA THV for VIV intervention in 19 mm, 21 mm, 23 mm, and 25 mm Epic, Mitroflow and Magna Ease bioprosthetic valves. Multimodality imaging and hydrodynamic evaluation was performed at each implantation depth. The 23 mm ALLEGRA valve had gradients <20 mmHg in the Mitroflow and Epic valves sized ≥21 mm, and in all sizes of the Magna Ease valve. Gradients did not increase significantly at lower implantation depths. The 19 mm Epic (+2 mm: 20.1±0.6 mmHg, -2 mm: 18.8±0.5 mmHg, -6 mm: 22.8±0.3 mmHg) and 19 mm Mitroflow (+2 mm: 24.1±0.2 mmHg, -2 mm: 31.5±0.3 mmHg, -6 mm: 25.6±0.2 mmHg) valves had elevated mean gradients. In larger sized surgical valves (≥23 mm) the regurgitant fraction was higher at low implantation depths. Pinwheeling was significantly worse in the smaller sized (≤21 mm) surgical valves and also at low (<-2 mm) implantation depth. CONCLUSIONS: The 23 mm ALLEGRA valve had favourable (<20 mmHg) gradients in all surgical valves sized ≥21 mm, even when the THV was implanted low. In 19 mm sized Mitroflow and Epic valves, gradients were elevated (>20 mmHg). While there was no major difference in mean transvalvular gradients, leaflet pinwheeling was worse at lower implantation depths.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".