Bibliographic record
Abstract
Mitral regurgitation (MR) is the most frequent valvular heart disease. One main cause of MR is abnormal papillary muscle (PM) displacement. Due to the limitations of previous aetiology studies, available therapies are often sub-optimal. In vitro simulation methods can aid in MR aetiology study, and new and existing therapy development. The aim of this work is to develop an in vitro platform including all mitral valve (MV) components and a flexible beating mock left ventricle made of silicone, for aetiology studies and MR therapy assessment. A novel in vitro simulation test rig has been developed to allow the positioning of animal MVs into a cardiovascular hydrodynamic testing system (ViVitro System, ViVitro Labs, Inc., Canada), and the control of MV sub-components. Functions of 3 MVs are measured at different PM positions, both at rest and during exercise. The system was also used to assess novel repair and replacement technologies. Results have shown that the MV functions are most sensitive to a specific form of PM displacement, associated with PM movements in the base-apical direction. The safe region of PM positions has been identified, which may serve as a benchmark and a potential guide for clinical corrections. Exercise has shown not to exacerbate MR fraction at any PM position, so exercise-induced MR appears not to be directly related to PM displacements. At a few PM positions, exercise caused MR volume per minute to reduce. The test rig can be used as a surgery rehearsal platform to enhance surgical outcome. A novel leaflet extension device and the prototype of a transcatheter MV replacement have both been tested in the test rig and implant displacement observed has been feedback for device improvement. A lab-made MV was tested as a promising approach to achieve customised generalised MV geometry.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".