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 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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".