Twelve-month results of a multicenter, blinded, pilot study of a novel peptide (B2A) in promoting lumbar spine fusion
Bibliographic record
Abstract
OBJECT: Failure of fusion after a transforaminal lumbar interbody fusion (TLIF) procedure is a challenging problem that can lead to ongoing low-back pain, dependence on pain medication, and inability to return to work. B2A is a synthetic peptide that has proven efficacy in achieving fusion in animal models and may have a better safety profile than bone morphogenetic protein. The authors undertook this study to evaluate the safety and efficacy of B2A peptide-enhanced ceramic granules (Prefix) in comparison with autogenous iliac crest bone graft (ICBG, control) in patients undergoing single-level TLIF. METHODS: Twenty-four patients with single-level degenerative disorders of the lumbar spine at L2-S1 requiring TLIF were enrolled between 2009 and 2010. They were randomly assigned to 3 groups: a control group (treated with ICBG, n = 9), a Prefix 150 group (treated with Prefix 150 μg/cm(3) granules, n = 8), and a Prefix 750 group (treated with Prefix 750 μg/cm(3) granules, n = 7). Outcome measures included the Oswestry Disability Index (ODI), visual analog pain scale, and radiographic fusion as assessed by CT and dynamic flexion/extension lumbar plain radiographs. RESULTS: At 12 months after surgery, the radiographic fusion rate was 100% in the Prefix 750 group, 78% in the control group, and 50% in the Prefix 150 group, although the difference was not statistically significant (p = 0.08). At 6 weeks the mean ODI score was 41.0 for the control group, 27.7 for the Prefix 750 group, and 32.2 for the Prefix 150 group, whereas at 12 months the mean ODI was 24.4 for control, 31.1 for Prefix 750, and 29.7 for Prefix 150 groups. Complications were evenly distributed among the groups. CONCLUSIONS: Prefix appears to provide a safe alternative to autogenous ICBG. Prefix 750 appears to show superior radiographic fusion when compared with autograft at 12 months after TLIF, although no statistically significant difference was demonstrated in this small study. Prefix and control groups both appeared to demonstrate comparable improvements to ODI at 12 months.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".