Fully porous 3D printed titanium femoral stem to reduce stress-shielding following total hip arthroplasty
Bibliographic record
Abstract
Current hip replacement femoral implants are made of fully solid materials which all have stiffness considerably higher than that of bone. This mechanical mismatch can cause significant bone resorption secondary to stress shielding, which can lead to serious complications such as peri-prosthetic fracture during or after revision surgery. In this work, a high strength fully porous material with tunable mechanical properties is introduced for use in hip replacement design. The implant macro geometry is based off of a short stem taper-wedge implant compatible with minimally invasive hip replacement surgery. The implant micro-architecture is fine-tuned to locally mimic bone tissue properties which results in minimum bone resorption secondary to stress shielding. We present a systematic approach for the design of a 3D printed fully porous hip implant that encompasses the whole activity spectrum of implant development, from concept generation, multiscale mechanics of porous materials, material architecture tailoring, to additive manufacturing, and performance assessment via in vitro experiments in composite femurs. We show that the fully porous implant with an optimized material micro-structure can reduce the amount of bone loss secondary to stress shielding by 75% compared to a fully solid implant. This result also agrees with those of the in vitro quasi-physiological experimental model and the corresponding finite element model for both the optimized fully porous and fully solid implant. These studies demonstrate the merit and the potential of tuning material architecture to achieve a substantial reduction of bone resorption secondary to stress shielding. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:1774-1783, 2017.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".