Femoral Head Lag Screw Position for Cephalomedullary Nails
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to determine if lag screw position affects the biomechanical properties of a cephalomedullary nail used to fix an unstable peritrochanteric fracture. METHODS: Unstable peritrochanteric fractures were created in 30 synthetic femurs and repaired with Long Gamma 3 Nails using one of 5 lag screw positions: superior, inferior, anterior, posterior, or central. Radiographic measurements including tip-apex distance and a calcar referenced tip-apex distance were calculated from anteroposterior and lateral radiographs. Specimens were tested for axial, lateral bending, and torsional stiffness and then loaded to failure in the axial position. Analysis of variance and linear regression were used for statistical analysis. RESULTS: The inferior lag screw position had significantly greater mean axial stiffness than superior (P < 0.01), anterior (P = 0.02), and posterior (P = 0.04) positions. Analysis revealed significantly less mean torsional stiffness for the superior lag screw position compared with other lag screw positions (P < 0.01 all 4 pairings). No statistical differences were noted for lateral bending stiffness. Superior and central lag screw positions had significantly greater mean load-to-failure than anterior (P < 0.01 and P = 0.02) and posterior (P < 0.01 and P = 0.05) positions. There were significant negative linear correlations between stiffness with distance from the calcar on anteroposterior radiographs and load-to-failure with distance from the center of femoral neck on the lateral radiographs. CONCLUSIONS: The inferior lag screw position produced the highest axial and torsional stiffness. Anterior and posterior lag screw positions produced the lowest stiffnesses and load-to-failure. Inferior placement of the lag screw on the anteroposterior radiograph and central placement on the lateral radiographs is recommended.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".