Development of a Biconcave Mobile‐Bearing Lumbar Total Disc Arthroplasty Concept Using Finite Element Analysis and Design Optimization
Bibliographic record
Abstract
Total disc arthroplasty (TDA) is a motion-preserving surgical treatment for spinal disorders. However, adverse surgical outcomes, such as abnormal kinematics, facet joint (FJ) overloading, and polyethylene (PE) failures, have limited wide application of lumbar TDAs. The objectives of this computational study were to elucidate how implant design and FJ articulation both influence metal-on-polyethylene (MoP) motion and contact mechanics, as well as to propose and refine a new mobile-bearing TDA concept which enhanced postoperative performance. Simulation results show that abnormal motions (lift-off and/or unsymmetrical motion) are alleviated in fixed-/mobile-bearing TDA-treated segments, as the FJ gap increases. It clearly demonstrates that FJ articulation guides segmental motion and interferes with intended MoP articulation. For an existing biconvex mobile-bearing design, component impingement leads to a peak PE stress of 20.8 MPa (yield stress: 13 MPa), indicating a high risk of PE creep/fracture. Therefore, we proposed a new TDA concept featuring a biconcave PE core with a smooth shape, in order to strengthen the PE rim and mitigate edge-loading. Furthermore, the biconcave-core TDA was optimally designed to promote normal segmental range of motion (ROM), or to minimize polyethylene contact pressure (PCP). In extension (the severest loading scenario), the biconvex-core TDA design caused a ROM 3.6° (+88%) greater than the intact segment and a peak PCP of 116.5 MPa. In contrast, ROM-optimal or PCP-optimal biconcave-core TDA designs decreased the ROM difference to 0.0° or the peak PCP to 24.3 MPa. Therefore, this new TDA design can potentially reduce the incidence of hypermotion and PE damage. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1805-1816, 2019.
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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) | 0.001 | 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) | 0.001 | 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".