Lifetime Estimation of Composite Bone Joint Screws
Bibliographic record
Abstract
Background: Bone screws are crucial elements in treating many types of open/closed fractures and arthroplasties in different joints in the body. The life time of many plates used in the fracture treatment are dependent on the screws function. The fracture of screws at any point of their lifetime will cause failure of the treatment for that specific pathology. This may lead to increase risk of new surgeries, osteomyelitis and less commonly septic arthritis. These complications not only have a negative impact on patient's quality of life, increase comorbidity and mortality but also increase health care cost significantly. Method: We study the lifetime of bone joint screws made up of biostable (polysulfone) and biosorbable (poly-lactide-co-glycolide) polymer composite materials. The lifetime estimations under in vitro conditions were calculated based on extremely small sample size. A computational intelligent model has been developed to estimate the lifetimes, which is superior to least square and real-coded Genetic Algorithm methods, specifically, for a small sample size of data. Retrospectively, 76 X-rays with screw fracture indication (37 polysulfon screws and 39 poly-lactide- co-glycolide screws) constitute the sample size in this study. The funding sources were provided by the office of research services at Ryerson University. Results: The proposed model is a robust method because it does not converge to a local optimum, and also it does not need the use of differential calculus facilitating the computational implementation. The findings make a significant contribution to reliability of composite implants. Conclusion: The application of this model for two types of composite materials used for bone joint screws proves that polysulfone screws lifetime is better than that of poly-lactide-co-glycolide screws. Therefore, using the polysulfone screws could decrease the health related complications such as new surgeries and osteomyelitis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".