An Effective Approach for Optimization of a Composite Intramedullary Nail for Treating Femoral Shaft Fractures
Bibliographic record
Abstract
The high stiffness of conventional intramedullary (IM) nails may result in stress shielding and subsequent bone loss following healing in long bone fractures. It can also delay union by reducing compressive loads at the fracture site, thereby inhibiting secondary bone healing. This paper introduces a new approach for the optimization of a fiber-reinforced composite nail made of carbon fiber (CF)/epoxy based on a combination of the classical laminate theory, beam theory, finite-element (FE) method, and bone remodeling model using irreversible thermodynamics. The optimization began by altering the composite stacking sequence and thickness to minimize axial stiffness, while maximizing torsional stiffness for a given range of bending stiffnesses. The selected candidates for the seven intervals of bending stiffness were then examined in an experimentally validated FE model to evaluate their mechanical performance in transverse and oblique femoral shaft fractures. It was found that the composite nail having an axial stiffness of 3.70 MN and bending and torsional stiffnesses of 70.3 and 70.9 N⋅m², respectively, showed an overall superiority compared to the other configurations. It increased compression at the fracture site by 344.9 N (31%) on average, while maintaining fracture stability through an average increase of only 0.6 mm (49%) in fracture shear movement in transverse and oblique fractures when compared to a conventional titanium-alloy nail. The long-term results obtained from the bone remodeling model suggest that the proposed composite IM nail reduces bone loss in the femoral shaft from 7.9% to 3.5% when compared to a conventional titanium-alloy nail. This study proposes a number of practical guidelines for the design of composite IM nails.
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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".