Pullout Analysis of a Lumbar Plate With Varying Screw Orientations
Bibliographic record
Abstract
STUDY DESIGN: Experimental and finite element analysis of anterior lumbar interbody fixation (ALIF) plate pullout. OBJECTIVE: The objective of this study was to determine the effect of screw angle and orientation on ALIF plate pullout strength. SUMMARY OF BACKGROUND DATA: It has been thought that angling the screws in an ALIF plate leads to better fixation strength; however, a few studies are published on this question, which produced conflicting results. METHODS: Using custom guides, screws were configured in 9 different orientations to affix ALIF plates to polyurethane foam blocks. Pullout tests were performed at a rate of 1 mm/min. In addition, finite element analyses were performed on a 2-dimensional screw-block model to gain insight into the internal stress during pullout. RESULTS: The pullout load was the greatest, with screws positioned 12° outward sagittaly and 6° inward coronally (936 ± 72 N). This orientation was statistically greater than the orientation with the lowest pullout load (812 ± 45 N, P < 0.05); however, no group was statistically different than placing the screws straight in (868 ± 86 N, P > 0.05). Finite elements analysis showed some gain in pullout strength at 12° followed by some loss at greater angles. As the screw insertion angle increased, stress levels elevated within the block even in the regions away from the screw. CONCLUSION: Significant difference was found between certain screw-angle configurations; however, when compared with simply placing the screws straight in, the difference was never more than 8%. This implies that there is greater freedom in the angle and placement of screws than previously thought. Our results show that there is little change in fixation strength when placing the screw in a different direction.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".