Effects of hip implant modular neck material and assembly method on fatigue life and distraction force
Bibliographic record
Abstract
Hip implant neck fractures and adverse tissue reactions associated with fretting-corrosion damage at modular interfaces are a major source of concern. Therefore, there is an urgent clinical need to develop accurate in vitro test procedures to better understand, predict and prevent in vivo implant failures. This study aimed to simulate in vivo fatigue fracture and distraction of modular necks in an in vitro setting, and to assess the effects of neck material (Ti6Al4V vs. CoCrMo) and assembly method (hand vs. impact) on the fatigue life and distraction of the necks. Fatigue tests were performed on the cementless PROFEMUR® Total Hip Modular Neck System under two different loads and number of cycles: 2.3 kN for 5 million cycles, and 7.0 kN for 1.3 million cycles. The developed in vitro simulation setup successfully reproduced in vivo modular neck fracture mode and location. Neck failure occurred at the neck-stem taper and the fracture ran from the distal lateral neck surface to the proximal medial entry point of the neck into the stem. None of the necks failed under the 2.3 kN load. However, all hand-assembled Ti6Al4V necks failed under the 7.0 kN load. In contrast, none of the hand-assembled CoCrMo necks and impact-assembled necks (Ti6Al4V or CoCrMo) failed under this higher load. In conclusion, Ti6Al4V necks were more susceptible to fatigue failure than CoCrMo necks. In addition, impact assembly substantially improved the fatigue life of Ti6Al4V necks and also led to overall higher distraction forces for both neck materials. Overall, this study shows that the material and assembly method can affect the fatigue strength of modular necks. Finally, improper implant assembly during surgery may result in diminished modular neck survivability and increased failure rates. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2023-2030, 2017.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".