Design and evaluation of surface conformance in additively manufactured spinal reconstruction implants
Bibliographic record
Abstract
Spinal implant surface conformance with the underlying bone is often critical to the long-term success of spinal surgeries; however, most spinal implants have a limited ability to match the organic surface profile of the vertebral endplate. To address this challenge, this study aimed to evaluate the surface conformance of additively manufactured lumbar intervertebral cages based on bone contact area and pressure distribution. Two lateral lumbar interbody fusion (LLIF) devices with either a solid or latticed core were designed for additive manufacturing (AM). The two implants were manufactured using SLA in resin with mechanical properties similar to polyether-ether-ketone (PEEK) and in a titanium alloy (Ti64) using laser powder bed fusion. Mechanical testing occurred on three sets of polyurethane Sawbones® machined to simulate a non-uniform vertebral endplate bone. Contact area and pressure distribution were measured using the Tekscan thin film sensor under lumbar compressive waveforms applied via an AMTI VIVO system. Conventional solid implants had an average surface coverage of 19% and 16%, 1 mm of displacement motion each, and peak pressure of 4.1 to 4.8 MPa, for resin and titanium respectively. The gyroid latticed implant had 13% and 11% surface coverage, 1.46 mm and 1 mm displacement motion, and lower of peak pressure for resin and titanium respectively. These results are consistent with previous studies which found that matching the implant to the vertebral endplate stiffness reduces risks of implant subsidence. In conclusion, this study provides preliminary support for the use of AM in tailoring implant design to conform to the endplate geometry. Future work should look to evaluate surface conformance in other designs as well as consideration for testing under fatigue loading conditions.
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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) | 0.000 | 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) | 0.000 | 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".