Evidence map›Paper›PMID 35476707›Full record

ArticlePloS one2022

Derivation of clinical prediction rules for identifying patients with non-acute low back pain who respond best to a lumbar stabilization exercise program at post-treatment and six-month follow-up.

Christian Larivière, Khalil Rabhi, Richard Preuss, Marie-France Coutu, Nicolas Roy, Sharon M Henry

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Deep learning for predicting lumbar segmental instability using neutral lateral lumbar radiographs: a retrospective study.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
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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

6 authors.

Christian LarivièreInstitut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST), Montréal, Québec, Canada.ORCID 0000-0003-4299-4755
Khalil RabhiIndependent Statistician Consultant, Montréal, Québec, Canada.
Richard PreussCenter for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Institut Universitaire sur la Réadaptation en Déficience Physique de Montréal (IURDPM), Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Ile-de-Montréal (CCSMTL), Montréal, Québec, Canada.
Marie-France CoutuCenter for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Institut Universitaire sur la Réadaptation en Déficience Physique de Montréal (IURDPM), Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Ile-de-Montréal (CCSMTL), Montréal, Québec, Canada.
Nicolas RoyCenter for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Institut Universitaire sur la Réadaptation en Déficience Physique de Montréal (IURDPM), Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Ile-de-Montréal (CCSMTL), Montréal, Québec, Canada.
Sharon M HenryDepartment of Neurological Sciences, University of Vermont, Burlington, Vermont, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Low back pain (LBP) remains one of the most common and incapacitating health conditions worldwide. Clinical guidelines recommend exercise programs after the acute phase, but clinical effects are modest when assessed at a population level. Research needs to determine who is likely to benefit from specific exercise interventions, based on clinical presentation. This study aimed to derive clinical prediction rules (CPRs) for treatment success, using a lumbar stabilization exercise program (LSEP), at the end of treatment and at six-month follow-up. The eight-week LSEP, including clinical sessions and home exercises, was completed by 110 participants with non-acute LBP, with 100 retained at the six-month follow-up. Physical (lumbar segmental instability, motor control impairments, posture and range of motion, trunk muscle endurance and physical performance tests) and psychological (related to fear-avoidance and home-exercise adherence) measures were collected at a baseline clinical exam. Multivariate logistic regression models were used to predict clinical success, as defined by ≥50% decrease in the Oswestry Disability Index. CPRs were derived for success at program completion (T8) and six-month follow-up (T34), negotiating between predictive ability and clinical usability. The chosen CPRs contained four (T8) and three (T34) clinical tests, all theoretically related to spinal instability, making these CPRs specific to the treatment provided (LSEP). The chosen CPRs provided a positive likelihood ratio of 17.9 (T8) and 8.2 (T34), when two or more tests were positive. When applying these CPRs, the probability of treatment success rose from 49% to 96% at T8 and from 53% to 92% at T34. These results support the further development of these CPRs by proceeding to the validation stage.

Indexed as

Joint InstabilityLow Back PainClinical Decision RulesExercise TherapyFollow-Up StudiesHumansLumbosacral Region

Identifiers

PMID35476707
PMCPMC9045609

What OpenQuestion 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 OpenQuestion graph.