Biomechanical Comparison of Subsidence Between Patient-Specific and Non-Patient-Specific Lumbar Interbody Fusion Cages
Bibliographic record
Abstract
Study Design Biomechanical study. Objectives Several strategies to improve the surface of contact between an interbody device and the endplate have been employed to attenuate the risk of cage subsidence. 3D-printed patient-specific cages have been presented as a promising alternative to help mitigate that risk, but there is a lack of biomechanical evidence supporting their use. We aim to evaluate the biomechanical performance of 3D printed patient-specific lumbar interbody fusion cages in relation to commercial cages in preventing subsidence. Methods A cadaveric model is used to investigate the possible advantage of 3D printed patient-specific cages matching the endplate contour using CT-scan imaging in preventing subsidence in relation to commercially available cages (Medtronic Fuse and Capstone). Peak failure force and stiffness were analyzed outcomes for both comparison groups. Results PS cages resulted in significantly higher construct stiffness when compared to both commercial cages tested (>59%). PS cage peak failure force was 64% higher when compared to Fuse cage ( P < .001) and 18% higher when compared to Capstone cage ( P = .086). Conclusions Patient-specific cages required higher compression forces to produce failure and increased the cage-endplate construct' stiffness, decreasing subsidence risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".