Can activated titanium interbody cages accelerate or enhance spinal fusion? a review of the literature and a design for clinical trials
Bibliographic record
Abstract
While spinal interbody cage options have proliferated in the past decade, relatively little work has been done to explore the comparative potential of biomaterial technologies in promoting stable fusion. Innovations such as micro-etching and nano-architectural designs have shown purported benefits in in vitro studies, but lack clinical data describing their optimal implementation. Here, we critically assess the pre-clinical data supportive of various commercially available interbody cage biomaterial, topographical, and structural designs. We describe in detail the osteointegrative and osteoconductive benefits conferred by these modifications with a focus on polyetheretherketone (PEEK) and titanium (Ti) interbody implants. Further, we describe the rationale and design for two randomized controlled trials, which aim to address the paucity of clinical data available by comparing interbody fusion outcomes between either PEEK or activated Ti lumbar interbody cages. Utilizing dual-energy computed tomography (DECT), these studies will evaluate the relative implant-bone integration and fusion rates achieved by either micro-etched Ti or standard PEEK interbody devices. Taken together, greater understanding of the relative osseointegration profile at the implant-bone interface of cages with distinct topographies will be crucial in guiding the rational design of further studies and innovations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".