Evidence map›Paper›PMID 36740609›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

Developing a triage predictive model for access to a spinal surgeon using clinical variables and natural language processing of radiology reports.

Brandon Krebs, Andrew Nataraj, Erin McCabe, Shannon Clark, Zahin Sufiyan, Shelby S Yamamoto, Osmar Zaïane, Douglas P Gross

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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. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Brandon KrebsFaculty of Rehabilitation Medicine, University of Alberta, Edmonton, Canada.
Andrew NatarajDepartment of Surgery, University of Alberta, Edmonton, Canada.
Erin McCabeFaculty of Rehabilitation Medicine, University of Alberta, Edmonton, Canada.
Shannon ClarkDepartment of Computing Science, University of Alberta, Edmonton, Canada.
Zahin SufiyanDepartment of Computing Science, University of Alberta, Edmonton, Canada.
Shelby S YamamotoSchool of Public Health, University of Alberta, Edmonton, Canada.
Osmar ZaïaneDepartment of Computing Science, University of Alberta, Edmonton, Canada.
Douglas P GrossDepartment of Physical Therapy, University of Alberta, 2-50 Corbett Hall, Alberta, Edmonton, T6G 2G4, Canada. dgross@ualberta.ca.ORCID 0000-0002-2427-6277

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo utilize natural language processing (NLP) of MRI reports and various clinical variables to develop a preliminary model predictive of the need for surgery in patients with low back and neck pain. Such a model would be beneficial for informing clinical practice decisions and help reduce the number of unnecessary surgical referrals, streamlining the surgical process.

methodsA historical cohort study was conducted using de-identified data from patients referred to a spine assessment clinic. Various demographic, clinical, and radiological variables were included as potential predictors. Full-text radiology reports of patients' MRI findings were vectorized using NLP before applying machine learning algorithms to develop models predicting who underwent surgery. Outputs from these models were then entered into a logistic regression model with clinical variables to develop a preliminary model predictive of surgical recommendations.

resultsOf the 398 patients assessed, 71 underwent spine surgery. NLP variables were significant predictors in univariate analysis but did not remain in the final logistic regression model. An outcome of receiving surgery was predicted by a primary symptom of low back and leg pain (adjusted odds ratio 2.81), distal pain indicated by a pain diagram (adjusted odds ratio 2.49) and self-reported difficulties walking (adjusted odds ratio 2.73).

conclusionA logistic regression model was created to predict which patients may require spine surgery. Simple clinical variables appeared more predictive than variables created using NLP. However, additional research with more data samples is needed to validate this model and fully evaluate the usefulness of NLP for this task.

Indexed as

Low Back PainNatural Language ProcessingNeck PainTriageAdultAgedCohort StudiesFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedBack and neck painPredictive factorsSpinal surgerySurgical outcome

Identifiers

PMID36740609

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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 OpenQuestion graph.