The Effect of Locking Head Inserts on the Biomechanical Properties of a 3.5-mm Broad Locking Compression Plate When Used in an Open Fracture-Gap Model
Bibliographic record
Abstract
Abstract Objective To determine the effect of locking head inserts (LHI) on plate strain, stiffness, and deformation when applied to a 3.5-mm broad locking compression plate (LCP) in an open fracture-gap model. Study Design Six, 13-hole, 3.5-mm broad LCP were secured to epoxy bone models with a 10 mm central defect and 1 mm plate offset. Two peripheral locking screws were placed in each segment, with the remaining screw holes left unfilled. Three strain gauges were glued to each LCP at anticipated regions of maximum strain. Constructs underwent cyclic uniaxial loading at a rate of 20 mm/min to 400 N in three different configurations (Configuration 1: no LHI, Configuration 2: 3 LHI, Configuration 3: 9 LHI). LHI were tightened to 4 Nm of torque. A data acquisition system was used to collect implant strain during testing. Construct stiffness and deformation were recorded by the biomechanical testing machine. Results Maximum implant strain was recorded at the central screw hole directly over the simulated fracture gap in all configurations (Mdn 1,837.3 µε [interquartile range: 1,805.1–1,862.0]). There was no difference in implant peak-to-peak strain with addition of LHI at all three gauges (Gauge 1 [p = 0.847], Gauge 2 [p = 0.847], Gauge 3 [p = 0.311]). Similarly, peak-to-peak displacement (p = 0.069) and axial construct stiffness (p = 0.311) did not change with the addition of LHI. Conclusion The addition of LHI to a 3.5-mm broad LCP construct was not shown to have an effect on plate strain, stiffness, or deformation.
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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 | 0.000 |
| 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) | 0.001 | 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".