A self-expandable nitinol frame for cable-driven parallel mechanisms in minimally invasive cardiovascular interventions
Bibliographic record
Abstract
The integration of self-expandable nitinol frames with cable-driven parallel mechanisms offers a promising advancement in minimally invasive cardiovascular interventions. This study presents the design, fabrication, and verification of a miniaturized self-expandable nitinol frame to enhance catheter tip steerability and navigation within complex vascular anatomies. The frame is reduced in size for delivery through 7-8 Fr sheaths while accommodating diverse vascular diameters, allowing up to a maximum expansion of 15 mm. Iterative design and parametric studies ensured robust vessel anchoring with minimal deflection to maintain catheter tip control accuracy. Extensive testing included finite element simulations and benchtop experiments. Crimping simulations confirmed that the maximum Von Mises stresses (575 MPa) did not exceed nitinol's yield stress, and deformation profiles matched experimental results. Deflection tests showed minimal deflections below 0.45 mm at the frame's anchoring points, ensuring precise tip control. Radial force studies validated balanced forces below 6 N (for target vessel diameters), preventing migration without damaging vessel walls. Friction studies demonstrated superior performance, reducing friction and enhancing force transmission efficiency. These findings indicated that the proposed miniaturized frame design is a feasible option for cardiovascular interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".