Evidence map›Paper›PMID 42564997›Full record

ArticleInterventional pain medicine2026

Sustained pain relief across modic subtypes following basivertebral nerve ablation: A real-world study.

Emily R Bellow, Justin N Passman, Derek Johnson, Jennifer Bae, Sandi Bajrami, Yvette Sol, William Caldwell

Abstract read
In one paragraph

Article in Interventional pain medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Emily R BellowDepartment of Anesthesiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Justin N PassmanDepartment of Anesthesiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Derek JohnsonDepartment of Anesthesiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Jennifer BaeDepartment of Anesthesiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Sandi BajramiDepartment of Anesthesiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Yvette SolDepartment of Anesthesiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
William CaldwellDepartment of Anesthesiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Basivertebral nerve (BVN) ablation has emerged as a safe and effective minimally invasive treatment for patients with vertebrogenic low back pain and corresponding Modic Changes on MRI. However, real-world evidence evaluating the impact of Modic Change subtype on clinical outcomes is limited. Objective: To evaluate the impact of Modic Change subtype on clinical outcomes following BVN ablation in a real-world cohort. Methods: Adult patients who underwent BVN ablation at our institution from November 2019 to January 2025 were included in this retrospective study. A total of 134 patients were classified by Modic Change type on their pre-procedural MRI and stratified into Type 1 (N = 59), Type 2 (N = 52), and Mixed (N = 23) Groups. Pain severity was assessed using the Visual Analog Scale (VAS) at baseline, 4-6 weeks, 6 months, and 12 months post-procedure. Results: A statistically significant decrease in VAS at 12 months was observed for the Type 1 Group (7.44 vs. 3.15, Conclusions: BVN ablation produced sustained, significant pain reduction and comparable clinical outcomes across Modic Change subtypes. These findings provide real-world evidence supporting the effectiveness of BVN ablation in patients with vertebrogenic low back pain.

Indexed as

Basivertebral nerve ablationChronic low back painIntracept procedureModic changesPain reductionVertebogenic pain

Identifiers

PMID42564997
PMCPMC13446038

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.