Clinical and Radiographic Outcomes of Anterior Lumbar Interbody Fusions Using a Titanium Cage with a Biomimetic Surface
Bibliographic record
Abstract
Abstract Background We analyzed clinical and radiographic outcomes in patients undergoing anterior lumbar interbody fusions (ALIFs) using a new biomimetic titanium fusion cage (Titan nanoLOCK interbody, Medtronic, Minneapolis, Minnesota, United States). This specialized cage employs precise nanotechnology to stimulate inherent biochemical and cellular osteogenic reactions to the implant, aiming to amplify the rate of fusion. To our knowledge, this is the only study to assess early clinical and radiographic results in ALIFs. Methods We conducted a retrospective review of data for patients who underwent single or multilevel ALIF using this implant between October 2016 and April 2021. Indications for treatment were spondylolisthesis, postlaminectomy syndrome, or spinal deformity. Clinical and radiographic outcome data for these patients were collected and assessed. Results A total of 84 patients were included. The mean clinical follow-up was 36.6 ± 14 months. At 6 months, solid fusion was seen in 97.6% of patients. At 12 months, solid fusion was seen in 98.8% of patients. Significant improvements were seen in patient-reported outcome measures (PROMs; visual analog scale and Oswestry Disability Index) at 6 and 12 months compared with the preoperative scores (p < 0.001). One patient required reoperation for broken pedicle screws 2 days after the ALIF. None of the patients required readmission within 90 days of surgery. No patients experienced an infection. Conclusions ALIF using a new titanium interbody fusion implant with a biomimetic surface technology demonstrated high fusion rates (97.6%) as early as 6 months. There was significant improvement in PROMs at 6 and 12 months.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".