JoVE Video Dataset
Bibliographic record
Abstract
The options for testing new cardiac procedures and investigative medical devices prior to use in an animal model are limited. In this study, we present a method for mounting a porcine aortic valve in a pulse duplicator to evaluate its hydrodynamic properties. These properties can then be evaluated before and after the procedure under investigation is performed and/or the investigative medical device is applied. Securing the inflow segment presents some difficulty owing to the lack of circumferential myocardium in the left ventricular outflow tract. This method addresses that issue by securing the inflow segment using the anterior leaflet of the mitral valve and then suturing the left ventricular free wall around the inflow fixture. The outflow segment is secured simply by inserting the fixture into an incision in the superior aspect of the aortic arch. We found that specimens had significantly different hydrodynamic properties before and after tissue fixation. This finding induced us to use fresh specimens in our testing and should be considered when using this method. In our work, we used this method to test novel intracardiac patch materials for use in the valvular position by performing an aortic valve neocuspidization procedure (Ozaki procedure) on the mounted porcine aortic valves. These valves were tested before and after the procedure to assess the change in hydrodynamic properties in comparison to the native valve. Herein, we report a platform for hydrodynamic testing of experimental aortic valve procedures that enables comparison with the native valve and between different devices and techniques used for the procedure under investigation.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.581 | 0.327 |
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".