Biomechanical analysis of pedicle screw density in spinal instrumentation for scoliosis treatment: first results
Bibliographic record
Abstract
Clinical studies reveal remarkable variation in screw patterns, or screw density in spinal instrumentation. Screw density may have a great impact on blood loss, operative time, radiation, risk of screw malposition, and cost. Thus, there is a need to understanding of the biomechanical effects of screw density so as to minimize the number of pedicle screws while ensuring safe and effective instrumentation. The objective of this study was to compare the deformity correction effects and bone-screw loadings of different pedicle screw densities in spinal instrumentation for scoliosis treatment. Spinal instrumentation simulations were performed on three scoliosis patients using 3 screw density patterns (low, preferred, and high screw density) proposed by two experienced surgeons and basic correction techniques: concave rod attachment, rod derotation, apical vertebral derotation, and convex side rod attachment. Simulation results showed that all tested screw densities generated quite similar correction, with differences between the achieved corrections all below 3°. The average bone-screw forces were 244±67N, 214±66 N, and 210±71 N, respectively for low, preferred, and high densities. It remains a complex challenge balancing the benefit of load sharing between more implants with the overconstraints and limited degrees of freedom introduced by the increased number of implants. Studies on additional screw densities and patterns proposed by more surgeons for a variety of cases, and using more diverse correction techniques are necessary to draw stronger conclusions and to recommend the optimal screw density.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".