Minimizing Pedicle Screw Pullout Risks
Bibliographic record
Abstract
STUDY DESIGN: Detailed biomechanical analysis of the anchorage performance provided by different pedicle screw designs and placement strategies under pullout loading. OBJECTIVE: To biomechanically characterize the specific effects of surgeon-specific pedicle screw design parameters on anchorage performance using a finite element model. SUMMARY OF BACKGROUND DATA: Pedicle screw fixation is commonly used in the treatment of spinal pathologies. However, there is little consensus on the selection of an optimal screw type, size, and insertion trajectory depending on vertebra dimension and shape. METHODS: Different screw diameters and lengths, threads, and insertion trajectories were computationally tested using a design of experiment approach. A detailed finite element model of an L3 vertebra was created including elastoplastic bone properties and contact interactions with the screws. Loads and boundary conditions were applied to the screws to simulate axial pullout tests. Force-displacement responses and internal stresses were analyzed to determine the specific effects of each parameter. RESULTS: The design of experiment analysis revealed significant effects (P<0.01) for all tested principal parameters along with the interactions between diameter and trajectory. Screw diameter had the greatest impact on anchorage performance. The best insertion trajectory to resist pullout involved placing the screw threads closer to the pedicle walls using the straightforward insertion technique, which showed the importance of the cortical layer grip. The simulated cylindrical single-lead thread screws presented better biomechanical anchorage than the conical dual-lead thread screws in axial loading conditions. CONCLUSIONS: The model made it possible to quantitatively measure the effects of both screw design characteristics and surgical choices, enabling to recommend strategies to improve single pedicle screw performance under axial loading.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".