A biomechanical comparison of static versus dynamic lag screw modes for cephalomedullary nails used to fix unstable peritrochanteric fractures
Bibliographic record
Abstract
BACKGROUND: The gamma nail has an option to statically lock its lag screw (static mode) or to allow its lag screw to move within the nail to compress the intertrochanteric fracture (dynamic mode). The purpose of this study was to compare the biomechanical stiffness of static and dynamic lag screw modes for a cephalomedullary nail used to fix an unstable peritrochanteric fracture. METHODS: Unstable four-part peritrochanteric fractures were created in 30 synthetic femurs and fixed with Long Gamma 3 Nails. Mechanical tests were conducted for axial, lateral, and torsional stiffness with intact femurs, femur-nail constructs with static lag screw mode,and femur-nail constructs with dynamic lag screw mode. A paired Student's t test was used for all statistical comparisons between test groups. RESULTS: Axial and torsional stiffness of intact femurs was significantly greater than femur-nail constructs (p < 0.01 all comparisons),whereas lateral stiffness was significantly less (p < 0.01 all comparisons). Axial stiffness of the femur-nail construct was significantly greater (p < 0.01) in static mode (484.3 N/mm 80.2 N/mm) than in dynamic mode (424.1 N/mm 78.0 N/mm).Lateral stiffness was significantly greater (p < 0.01) in static mode (113.9 N/mm 8.4 N/mm) than in dynamic mode (109.5N/mm 8.8 N/mm). Torsional stiffness was significantly greater (p = 0.02) in dynamic mode (114.5 N/mm 28.2 N/mm) than in static mode (111.7 N/mm 27.0 N/mm). DISCUSSION: There is a 60 N/mm (12.4%) reduction in axial stiffness when the lag screw is in dynamic mode. Given the statistically significant reduction in axial and lateral stiffness with use of the dynamic mode, static lag screw mode should be further explored clinically for treatment of unstable peritrochanteric fractures.
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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 | 0.000 |
| 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".