Biomechanical assessment of composite versus metallic intramedullary nailing system in femoral shaft fractures: A finite element study
Bibliographic record
Abstract
BACKGROUND: Intramedullary nails are the primary choice for treating long bone fractures. However, complications following nail surgery including non-union, delayed union, and fracture of the bone or the implant still exist. Reducing nail stiffness while still maintaining sufficient stability seems to be the ideal solution to overcome the abovementioned complications. METHODS: In this study, a new hybrid concept for nails made of carbon fibers/flax/epoxy was developed in order to reduce stress shielding. The mechanical performance of this new implant in terms of fracture stability and load sharing was assessed using a comprehensive non-linear FE model. This model considers several mechanical factors in nine fracture configurations at immediately post-operative, and in the healed bone stages. RESULTS: Post-operative results showed that the hybrid composite nail increases the average normal force at the fracture site by 319.23N (P<0.05), and the mean stress in the vicinity of fracture by 2.11MPa (P<0.05) at 45% gait cycle, while only 0.33mm and 0.39mm (P<0.05) increases in the fracture opening and the fragments' shear movement were observed. The healed bone results revealed that implantation of the titanium nail caused 20.2% reduction in bone stiffness, while the composite nail lowered the stiffness by 11.8% as compared to an intact femur. INTERPRETATION: Our results suggest that the composite nail can provide a preferred mechanical environment for healing, particularly in transverse shaft fractures. This may help bioengineers better understand the biomechanics of fracture healing, and aid in the design of effective implants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".