Evaluation of the contact surface between vertebral endplate and 3D printed patient-specific cage vs commercial cage
Bibliographic record
Abstract
Abstract Biomechanical study. To evaluate the performance of the contact surface for 3D printed patient-specific cages using CT-scan 3D endplate reconstructions in comparison to the contact surface of commercial cages. Previous strategies to improve the surface of contact between the device and the endplate have been employed to attenuate the risk of cage subsidence. Patient-specific cages have been used to help, but only finite-element studies have evaluated the effectiveness of this approach. There is a possible mismatch between the CT-scan endplate image used to generate the cage and the real bony endplate anatomy that could limit the performance of the cages. A cadaveric model is used to investigate the possible mismatch between 3D printed patient-specific cages and the endplate and compare them to commercially available cages (Medtronic Fuse and Capstone). Contact area and contact stress were used as outcomes. When PS cage was compared to the Capstone cage, the mean contact area obtained was 100 ± 23.6 mm2 and 57.5 ± 13.7 mm2, respectively (p < 0.001). When compared to the Fuse cage, the mean contact area was 104.8 ± 39.6 mm2 and 55.2 ± 35.1 mm2, respectively(p < 0.001). Patient-specific cages improve the contact area between the implant and the endplate surface, reducing the contact stress and the risk of implant subsidence during LIF surgeries.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".