Biomechanical design using in-vitro finite element modeling of distal femur fracture plates made from semi-rigid materials versus traditional metals for post-operative toe-touch weight-bearing
Bibliographic record
Abstract
This proof-of-concept study designs distal femur fracture plates from semi-rigid materials vs. traditional metals for toe-touch weight-bearing recommended to patients immediately after surgery. The two-fold goal was to (a) reduce stress shielding (SS) by increasing cortical bone stress thereby reducing the risk of bone absorption and plate loosening, and (b) reduce delayed healing (DH) via early callus formation by optimizing axial interfragmentary motion (AIM). Finite element analysis was used to design semi-rigid plates whose elastic moduli E ensured plates permitted AIM of 0.2 - 1 mm for early callus formation. A low hip joint force of 700 N (i.e. 100% x body weight) was applied, which corresponds to a typical 140 N toe-touch foot-to-ground force (i.e. 20% x body weight) recommended to patients after surgery. Analysis was done using 2 screw materials (steel or titanium) and types (locked or non-locked). Steel and titanium plates were also analyzed. Semi-rigid plates (vs. metal plates) had lower overall femur/plate construct stiffnesses of 508 - 1482 N/mm, higher cortical bone stresses under the plate by 2.02x - 3.27x thereby reducing SS, and lower E values of 414 - 2302 MPa to permit AIM of 0.2 - 1 mm thereby reducing DH.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".