Review of Biomechanical Studies and Finite Element Modeling of Sternal Closure Using Bio-Active Adhesives
Bibliographic record
Abstract
<p>The most common complication of median sternotomy surgery is sternum re-separation after sternal fixation, which leads to high rates of morbidity and mortality. The adhered sternal fixation technique comprises the wiring fixation technique and the use of bio-adhesives. Adhered sternal fixation techniques have not been extensively studied using finite element analysis, so mechanical testing studies and finite element analysis of sternal fixation will be presented in this review to find the optimum techniques for simulating sternal fixation with adhesives. The optimal wiring technique should enhance bone stability and limit sternal displacement. Bio-adhesives have been proposed to support sternal fixation, as wiring is prone to failure in cases of post-operative problems. The aim of this paper is to review and present the existing numerical and biomechanical sternal fixation studies by reviewing common sternal closure techniques, adhesives for sternal closure, biomechanical modeling of sternal fixation, and finite element modeling of sternal fixation systems. Investigating the physical behavior of 3D sternal fixation models by finite element analysis (FEA) will lower the expense of conducting clinical trials. This indicates that FEA studies of sternal fixation with adhesives are needed to analyze the efficiency of this sternal closure technique virtually.</p>
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".