The effectiveness of basivertebral nerve radiofrequency ablation for the treatment of vertebrogenic low back pain: 1-year results of a prospective real-world cohort study
Bibliographic record
Abstract
SUMMARY OF BACKGROUND DATA: Multiple clinical trials have demonstrated the effectiveness of intraosseous basivertebral nerve radiofrequency ablation (BVNA) for treating chronic vertebrogenic low back pain (vLBP). Few studies have evaluated the effectiveness in a real-world population. OBJECTIVES: To evaluate the effectiveness of BVNA for vLBP in a real-world population. METHODS: A single-arm prospective cohort study of patients with LBP ≥ 6 months and Type 1 or Type 2 Modic changes on MRI. The primary outcome was mean improvement in Oswestry Disability Index (ODI) post-BVNA. Secondary outcomes included the proportion of participants with (1) ≥30% and ≥15-point ODI improvements, (2) ≥2-point and ≥50% reductions in pain on Numerical Rating Scale (NRS), and (3) ≥ "much improved" on Patient Global Impression of Change (PGIC) at 3- and 12-month follow-up. RESULTS: In total, 60 participants were included (mean age 57.0 ± 13.4 years; 45.0% female). Mean ODI score improvement was 13.9 ± 18.7 and 14.0 ± 15.7 points at 3- and 12-month follow-up, respectively. At 12 months, 52.8% (95% CI, 39.7-65.6) of participants reported ≥30% ODI improvement and 39.6% (95% CI, 27.6-53.1) of participants reported ≥15-point improvement in ODI. Twelve-month responder rates for ≥2-point and ≥50% NRS improvement were 67.9% (95% CI, 54.5-78.9) and 49.1% (95% CI, 36.1-62.1). Moreover, 54.7% (95% CI, 41.5-67.3) of participants reported being "much or very much improved" on the PGIC at 12 months. DISCUSSION/CONCLUSION: In this real-world cohort, over half of participants with vLBP experienced clinically meaningful improvements in pain and function at 12-month post-BVNA. TRIAL REGISTRATION DETAILS: ClinicalTrials.gov (original study: NCT04449835; continuation study: NCT05660512); [original study: June 25, 2020; continuation study: December 13, 2022].
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".