Evidence map›Paper›PMID 32440606›Full record

ReviewPain reports

Framework for improving outcome prediction for acute to chronic low back pain transitions.

Steven Z George, Trevor A Lentz, Jason M Beneciuk, Nrupen A Bhavsar, Jennifer M Mundt, Jeff Boissoneault

Abstract readReview
In one paragraph

Review in Pain reports. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

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

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  15. Emerging Topics in the Behavioral Neuroscience of Tinnitus.Current topics in behavioral neurosciences · 2021
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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.

Steven Z GeorgeDepartment of Orthopaedic Surgery, Duke Clinical Research Institute, Duke University, Durham, NC, USA.
Trevor A LentzDepartment of Orthopaedic Surgery, Duke Clinical Research Institute, Duke University, Durham, NC, USA.
Jason M BeneciukDepartment of Physical Therapy, University of Florida, Gainesville, FL, USA.
Nrupen A BhavsarDivision of General Internal Medicine, Duke University, Durham, NC, USA.
Jennifer M MundtDepartment of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Jeff BoissoneaultDepartment of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical practice guidelines and the Federal Pain Research Strategy (United States) have recently highlighted research priorities to lessen the public health impact of low back pain (LBP). It may be necessary to improve existing predictive approaches to meet these research priorities for the transition from acute to chronic LBP. In this article, we first present a mapping review of previous studies investigating this transition and, from the characterization of the mapping review, present a predictive framework that accounts for limitations in the identified studies. Potential advantages of implementing this predictive framework are further considered. These advantages include (1) leveraging routinely collected health care data to improve prediction of the development of chronic LBP and (2) facilitating use of advanced analytical approaches that may improve prediction accuracy. Furthermore, successful implementation of this predictive framework in the electronic health record would allow for widespread testing of accuracy resulting in validated clinical decision aids for predicting chronic LBP development.

Indexed as

Chronic painOutcome predictionPain research

Identifiers

PMID32440606
PMCPMC7209816

What OpenQuestion holds

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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.