Transforaminal Lumbar Interbody Fusion (TLIF) with Expandable Banana-Shaped Interbody Spacers—Institutional 5-Year Experience
Bibliographic record
Abstract
Background: Transforaminal lumbar interbody fusion (TLIF) with static cages is a frequently performed procedure. Larger series focusing on the use of expandable TLIF spacers are less common. Methods: This retrospective, single-center observational cohort study reviewed consecutive patients treated by TLIF using expandable titanium interbody implants (ALTERA™, Globus Medical Inc., Audubon, PA, USA) for degenerative pathologies from L2-S1 between 11/2018 and 09/2023. Surgical parameters, adverse events, radiological outcomes (fusion rate, segmental lordosis, spinopelvic parameters), and clinical outcomes were analyzed through a mean postoperative follow-up of 12 months. Results: This study identified 270 patients (mean age 65 years, 50.4% female) who underwent TLIF with expandable interbody spacers at 324 levels. Clinical outcomes were good or excellent in 74.1% of patients at 3 months and 71.8% at 12 months. Radiographic fusion was achieved in 73.1% of assessable segments at 12 months. Segmental lordosis increased significantly from 17.8° preoperatively to 20.0° at 12 months (p < 0.001). Adverse event (AE) rates were acceptable across all timepoints, with no device failures or device-associated complications observed. Conclusions: This study demonstrates that TLIF with expandable titanium interbody implants was safe, associated with high fusion rates, and enabled significant restoration of segmental lordosis that was maintained during follow-up.
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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 | 0.001 |
| 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) | 0.001 | 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".