Design and development of a catheter-based tunable device for percutaneous mitral repair through coronary sinus
Bibliographic record
Abstract
According to the American Heart Association, the cardiac disease accounts for over 800,000 deaths every year (1 of every 3 deaths) in the US alone. Mitral regurgitation, which occurs in 2% of the population, has become the dominant valvular disease contributing to the high death rate caused by cardiac disease. The existing percutaneous treatments of mitral regurgitation suffer from compression of left circumflex artery, limiting their performance and causing serious iatrogenic consequences. Moreover, they are not tunable resulting in limited functionality. In this thesis, a catheter-based tunable device is designed to be implanted inside the coronary sinus for improving mitral regurgitation grade while minimizing the applied force on the left circumflex artery. A comprehensive computed tomography scan image analysis and experiments are performed to extract the required information for the design of the device and its evaluation with FEM simulations. A new effective engagement mechanism for integrating the device with the steerable catheter is designed and tested through large-scale experiments. Additionally, a temperature insensitive force/torque sensor is designed for guiding and introducing the device. This sensor can also be used in other catheter-based devices such as cardiac ablation catheters. The sensing structure of the sensor and its sensing method are evaluated by FEM simulations and large-scale prototyping. The actual-scale prototype of the sensor is fabricated, and the experiments are performed for analyzing the static and dynamic response of the sensor and its temperature cross-sensitivity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".