Titanium interlayer-mediated hydroxyapatite-coated polyetheretherketone cage in transforaminal lumbar interbody fusion surgery
Bibliographic record
Abstract
BACKGROUND: The variance in clinical responses to polyetheretherketone (PEEK) cages with titanium (Ti) and hydroxyapatite (HA) coatings (PEEK-Ti-HA cages) is still not clear. In this study, we aimed to evaluate the radiographic and clinical outcomes of patients undergoing TLIF using PEEK-Ti-HA cages with a particular focus on fusion rate. METHODS: A prospective and nonrandomized study was conducted to compare the outcomes of PEEK-Ti-HA cages (group A, n = 32) and uncoated PEEK cages (group B, n = 32). The follow up time was at least 2 years. The radiographic assessments included the regional lordosis (RL), disc height (DH), and fusion rate. The clinical indexes included the Japanese Orthopedic Association (JOA) scores and visual analog scale (VAS) scores (back and leg). RESULTS: No significant differences were found in the pre- and postoperative RL and DH between Group A and Group B. And RL and DH, even if there were any variance initially, were restored not long after surgery in both groups. Though Group A had a significantly higher fusion rate than group B at 3 months post-surgery (93.7% vs. 75.0%), the fusion rates for the two groups reached the same level (100%) when it comes to the final follow-up. Additionally, differences of VAS and JOA scores for the two groups in general approximate. CONCLUSIONS: PEEK-Ti-HA cages, in contrast with uncoated PEEK cages, produced a better fusion rate at 3 months after single-level TLIF. The fusion rates of both groups could get 100% at the final follow-up. PEEK-Ti-HA cages could achieve similar RL, DH, JOA scores and VAS scores in comparison with uncoated PEEK cages post-surgery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".