Evidence map›Paper›PMID 41762255›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

ISSLS Prize in Clinical Science 2026: Data-driven classification of lumbar spine degeneration trajectories in chronic low back pain.

Terence P McSweeney, Zehra Akkaya, Jiamin Zhou, Po-Hung Wu, Noah B Bonnheim, Thomas M Link, Jaro Karppinen, Jeffrey C Lotz, Simo Saarakkala, Aaron J Fields and 1 more

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Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Terence P McSweeneyResearch Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland. terence.mcsweeney@oulu.fi.
Zehra AkkayaDepartment of Radiology, Ankara University, Ankara, Turkey.
Jiamin ZhouDepartment of Orthopaedic Surgery, University of California, San Francisco, San Francisco, USA.
Po-Hung WuDepartment of Orthopaedic Surgery, University of California, San Francisco, San Francisco, USA.
Noah B BonnheimDepartment of Orthopaedic Surgery, Wake Forest University School of Medicine, Charlotte, USA.
Thomas M LinkDepartment of Radiology and Biomedical Engineering, University of California, San Francisco, San Francisco, USA.
Jaro KarppinenResearch Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Jeffrey C LotzDepartment of Orthopaedic Surgery, University of California, San Francisco, San Francisco, USA.
Simo SaarakkalaResearch Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Aaron J FieldsDepartment of Orthopaedic Surgery, University of California, San Francisco, San Francisco, USA.
Aleksei TiulpinResearch Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe temporal sequence of lumbar spine degeneration (trajectory) is difficult to characterize due to limited availability of longitudinal imaging data. The aim of this cross-sectional study was to discover degeneration trajectories and their clinical relevance by applying an innovative computational approach to an extensive dataset from chronic low back pain patients.

methodsClinical MRI exams from 423 patients in the comeBACK study were graded for disc degeneration, facet osteoarthritis, and other pathoanatomical features. We then trained an event-based model, which is specifically designed to infer longitudinal trajectories from cross-sectional data, to model spine degeneration trajectory subtypes. The clinical significance of the identified trajectories was assessed using propensity score matching of trajectory subtypes and subsequent generalized linear mixed-effects modeling. Pain characteristics included the Fear-Avoidance Beliefs Questionnaire, neuropathic pain (painDETECT), pain impact score, and chronic widespread pain (CWP).

resultsTwo distinct trajectories were identified. A "disc-first" subtype (n = 260, 61%) was characterized by a high prevalence of disc herniation and disc degeneration that was more severe than facet osteoarthritis. Conversely, a "facet-first" subtype (n = 146, 39%) was characterized by a greater severity of facet osteoarthritis than disc degeneration. The disc-first subtype was associated with more CWP (p = 0.030), while the facet-first subtype had higher neuropathic pain (painDETECT scores) (p = 0.039).

conclusionWe identified two distinct trajectories of lumbar spine degeneration related to differing clinical presentations. After replication with other large datasets, this method could be used to characterize and stage spinal degeneration of individual patients. Ultimately, this new information would help clarify degeneration mechanisms and risk factors and support treatment optimization.

Indexed as

Chronic PainIntervertebral Disc DegenerationLow Back PainLumbar VertebraeAdultCross-Sectional StudiesFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedOsteoarthritis, SpineDisease progressionIntervertebral disc degenerationLow back painOsteoarthritisSpine

Identifiers

PMID41762255

What Socratic holds

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Registered trials

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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 Socratic graph.