Development of a composite intramedullary nail for treating femoral shaft fractures
Bibliographic record
Abstract
Intramedullary nails are the primary choice for treating long bone fractures. However, the high axial rigidity of conventional nails, can significantly reduce compression at the fracture site, and thereby inhibit bone healing. It can also lead to subsequent bone loss upon fracture healing. Fibrereinforced composites have been suggested as an alternative material of choice in the design of fracture fixation implants to address these drawbacks. There are very few studies in the literature on the use of composite materials for intramedullary nails. In particular, there are no known studies which have considered the optimization of such implants to fulfill the requirements of a proper fracture healing. The purpose of the current thesis is to develop a composite intramedullary nail made of carbon-fibre/epoxy whose structure is optimized to provide a preferred mechanical environment for fracture healing. The thickness and stacking sequence of the composite tube were optimized using closed-form expressions for structural rigidities of a composite tube to minimize axial rigidity of the structure, while minimally sacrificing the bending and torsional rigidities. The actual performance of the best nail candidates inside the human femur was then examined in an experimentally validated finite element model. It was found that a composite nail with an outer diameter of 14 mm and a stacking sequence of [0 / -45 / 45 / -45 / 0 / -45 / 45 / -45 / 45 / -45 / 90 ] showed an overall superiority compared to the other configurations. The expressions for rigidity yielded an axial rigidity of 3.7MN , and bending and torsional rigidities of 70.3 and 70.9 2 N.m , respectively, which correlated well with the results of mechanical testing on the manufactured specimens (i.e. 3.74±0.05 MN, 66.9±1.0 2 N.m , and 70.7±2.0 2 N.m for axial, bending, and torsional rigidities, respectively). The manufactured composite specimens showed high strength in tension (403.9±7.8 MPa), compression (316.9±10.9 MPa), bending (405.3±8.1 MPa), and torsion (328.5±7.3 MPa). Moreover, a fatigue limit of 27 kN was obtained for the composite nail. The stiffness of the composite nail was found to remain almost constant versus the number of cycles. Overall, the findings of this thesis suggest that the proposed intramedullary nail is a potential candidate for use as an alternative to the conventional intramedullary 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".