The biomechanics of three different fracture fixation implants for distal femur repair in the presence of a tumor-like defect
Bibliographic record
Abstract
The femur is the most common long bone involved in metastatic disease. There is consensus about treating diaphyseal and epiphyseal metastatic lesions. However, the choice of device for optimal fixation for distal femur metaphyseal metastatic lesion remains unclear. This study compared the mechanical stiffness and strength of three different fixation methods. In 15 synthetic femurs, a spherical tumor-like defect was created in the lateral metaphyseal region, occupying 50% of the circumference of the bone. The defect was filled with bone cement and fixed with one of three methods: Group 1 (retrograde nail), Group 2 (lateral locking plate), and Group 3 (lateral nonlocking periarticular plate). Constructs were tested for mechanical stiffness and strength. There were no differences between groups for axial stiffness (Group 1, 1280 +/- 112 N/mm; Group 2, 1422 +/- 117 N/mm; and Group 3, 1403 +/- 122N/mm; p = 0.157) and offset torsional strength (Group 1, 1696 +/- 628N; Group 2, 1771 +/- 290N; and Group 3, 1599 +/- 253 N; p = 0.816). In the coronal plane, Group 2 (296 +/- 17 N/mm) had a higher stiffness than Group 1 (263 +/- 17N/mm; p = 0.018). In the sagittal plane, Group 1 (315 +/- 9 N/mm) had a higher stiffness than Group 3 (285 +/- 19 N/mm; p = 0.028). For offset torsional stiffness, Group 1 (256 +/- 23 N/mm) had a higher value than Group 3 (218 +/- 16 N/mm; p = 0.038). Group 1 had equivalent performance to both plating groups in two test modes, and it was superior to Group 3 in two other test modes. Since a retrograde nail (i.e. Group 1) would require less soft-tissue stripping in a clinical context, it may be the optimal choice for tumor-like defects in the distal femur.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".