Evidence map›Paper›PMID 39540649›Full record

Observational studyThe Journal of manual & manipulative therapy2025

Use of machine learning to identify prognostic variables for outcomes in chronic low back pain treatment: a retrospective analysis.

Carolyn Cheema, Jonathan Baldwin, Jason Rodeghero, Mark W Werneke, Jerry E Mioduski, Lynn Jeffries, Joseph Kucksdorf, Mark Shepherd, Carol Dionne, Ken Randall

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in The Journal of manual & manipulative therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Potential applications of artificial intelligence in pain management: a scoping review.Frontiers in pain research (Lausanne, Switzerland) · 2026
    Pooled it
  2. Article
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

10 authors.

Carolyn CheemaCollege of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, USA.ORCID 0000-0001-7099-8847
Jonathan BaldwinCollege of Allied Health, Department of Medical Imaging and Radiation Sciences, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
Jason RodegheroDepartment of Public Health & Community Medicine, School of Medicine, Tufts University, Boston, MA, USA.
Mark W WernekeNet Health Systems, Inc, Pittsburgh, PA, USA.
Jerry E MioduskiNet Health Systems, Inc, Pittsburgh, PA, USA.
Lynn JeffriesCollege of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, USA.
Joseph KucksdorfBellin Health, Orthopedics and Sports Medicine, Green Bay, WI, USA.
Mark ShepherdPhysical Therapy Department Bellin College, Green Bay, WI, USA.
Carol DionneCollege of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, USA.
Ken RandallCollege of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesMost patients seen in physical therapy (PT) clinics for low back pain (LBP) are treated for chronic low back pain (CLBP), yet PT interventions suggest minimal effectiveness. The Cochrane Back Review Group proposed 'Holy Grail' questions, one being: 'What are the most important (preventable) predictors of chronicity' for patients with LBP? Subsequently, prognostic factors influencing outcomes for CLBP have been described, however results remain conflicting due to methodological weaknesses.

methodsThis retrospective observational cohort study examined prognostic risk factors for PT outcomes in CLBP treatment using a sub-type of AI. Bootstrap random forest supervised machine learning analysis was employed to identify the outcomes-associated variables.

resultsThe top variables identified as predictive were: FOTO™ predicted functional status (FS) change score; FOTO™ predicted number of visits; initial FS score, age; history of jogging/walking, obesity, and previous treatments; provider education level; medication use; gender.

conclusionThis article presents how AI can be used to predict risk prognostic factors in healthcare research. Improving predictive accuracy helps clinicians predict outcomes and determine most appropriate plans of care and may impact research attrition rates.

Indexed as

Chronic PainLow Back PainMachine LearningPhysical Therapy ModalitiesAdultFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesRisk FactorsTreatment Outcomeartificial intelligencechronic low back painMachine learningphysical therapyprediction

Identifiers

PMID39540649
PMCPMC11770874

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.