Cortico-cancellous osseointegration into additively manufactured titanium implants using a load-bearing femoral ovine model
Bibliographic record
Abstract
Introduction: Titanium-based implants can be used to fill voids in bone reconstruction surgery. Through additive manufacturing (AM), it is possible to produce titanium implants with osteoconductive properties such as high porosity and low stiffness. AM facilitates a level of design flexibility and personalization that is not feasible with traditional techniques. Methods: In this study, osseointegration into titanium alloy (Ti-6Al-4V) lattices was investigated for 12 weeks post-implantation using a novel bicortical load-bearing ovine model. The objective was to assess the safety and efficacy of AM-fabricated implants using two lattice structures of contrasting stiffness spanning the full width of the femoral condyle. Results: This was achieved by evaluating implant osseointegration and bone–implant contact properties by histomorphometry, scoring local implant tissue responses via histopathology, and micro-computed tomography reconstruction. Discussion: We found that Ti-6Al-4V implants facilitated widespread and extensive osseointegration, with bone maturation ongoing at the conclusion of the trial period. Following the implantation period, no adverse clinical indications that could be directly ascribed to the presence of the implanted device were identified, as determined by macroscopic and microscopic observation.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".