J Occup Rehabil. 2017 (Sep); 27 (3): 445–455 ~ FULL TEXT
Heather M. Shearer, Pierre Cote, Eleanor Boyle, Jill A. Hayden, John Frank, William G. Johnson
UOIT-CMCC Center for the Study of Disability Prevention and Rehabilitation,
University of Ontario Institute of Technology,
2000 Simcoe St. North,
Oshawa, ON, L1H 7K4, Canada.
Purpose Our objective was to develop a clinical prediction model to identify workers with sustainable employment following an episode of work-related low back pain (LBP).
Methods We used data from a cohort study of injured workers with incident LBP claims in the USA to predict employment patterns 1 and 6 months following a workers' compensation claim. We developed three sequential models to determine the contribution of three domains of variables:
(1) basic demographic/clinical variables;
(2) health-related variables; and
(3) work-related factors.
Multivariable logistic regression was used to develop the predictive models. We constructed receiver operator curves and used the c-index to measure predictive accuracy.
Results Seventy-nine percent and 77 % of workers had sustainable employment at 1 and 6 months, respectively. Sustainable employment at 1 month was predicted by initial back pain intensity, mental health-related quality of life, claim litigation and employer type (c-index = 0.77). At 6 months, sustainable employment was predicted by physical and mental health-related quality of life, claim litigation and employer type (c-index = 0.77). Adding health-related and work-related variables to models improved predictive accuracy by 8.5 and 10 % at 1 and 6 months respectively.
Conclusion We developed clinically-relevant models to predict sustainable employment in injured workers who made a workers' compensation claim for LBP. Inquiring about back pain intensity, physical and mental health-related quality of life, claim litigation and employer type may be beneficial in developing programs of care. Our models need to be validated in other populations.
KEYWORDS:   Back injuries; Employment; Return to work
From the FULL TEXT Article:
Low back pain (LBP) is the highest contributor of global
disability worldwide.  In the UK in 2009, the direct
health care costs of chronic back pain exceeded £1.5 billion.  Occupational back pain is associated with significant
direct and indirect costs to employers, insurers, and injured
workers.  In 2013 in the United States, the incidence of
back pain injuries among full-time workers was 20 per
10,000.  Additionally, back problems were ranked seventh
for annual average cost of productivity loss (absenteeism
and presenteeism) among employed workers with
health reimbursement accounts.  A median of seven
days away from work to recuperate from a back injury was
reported in the United States in 2013. 
Previous studies suggest that back pain is episodic and is
associated with recurrent work absenteeism. [3, 6, 7]
Additionally, individuals with prevalent back pain are more
likely to report future back injuries, work absences, and
more comorbidities compared to workers with incident
LBP.  Although we have gained knowledge about the
outcomes experienced by injured workers with back pain,
we still lack the ability to accurately predict who is more
likely to regain sustainable employment. Therefore, it is
important to develop clinical prediction models to predict
outcome following a work-related back injury.
A challenging issue facing clinicians is to predict,
shortly after the injury, the probability of returning to
sustainable employment. Clinicians often rely on clinical
experience and clinically relevant scientific evidence to
determine the prognosis of a patient. Very few prediction
models clearly identify who is likely to return to work and
maintain sustainable employment following an occupational
low back injury. Several studies have proposed
prediction rules to identify workers at risk of chronic work
disability; however, their predictive ability is low, ranging
from 12 to 30 %. [9–11] Additionally, many models do not
include important prognostic factors for work disability.  A literature review of predictive models and rules
used to determine persisting functional restrictions in
individuals with sub-acute LBP, reported the predictive
ability of existing models was moderate at best (explained
variance ranged from 28 % to 51 %).  The objective of
our study was to develop a clinical prediction model to
identify individuals with sustainable patterns of employment
1 and 6 months after a low back work injury.
We used data from the Arizona State University Healthy
Back Study (ASU study) to develop clinical prediction
models. The ASU study was a prospective inception cohort
of workers who filed an incident claim for work-related
back pain between January 1, 1999 and June 30, 2002. 
The individuals worked for one of five different employers
across 37 American states.
To be included in the ASU study, workers had to meet to
(1) back pain reported to the employer;
(2) back injury occurred at work; and
(3) diagnosis of LBP based on ICD-9 codes included in the following categories:
721 (spondylosis and allied disorders),
722 (intervertebral disc disorders),
724 (other and unspecified disorders of back), and
847 (sprains and strains of other and unspecified parts of the back)
reported by the primary healthcare provider to the insurer.
Workers with fractures, denied workers'
compensation claim, subsequent claim during the
study period, or litigation related to the back injury that
was initiated prior to study enrollment were excluded. 
The intent of our clinical prediction models were to
predict sustainable employment using data collected
shortly after the onset of a workers' compensation claim
for LBP. Therefore, we restricted our sample to participants
who completed the baseline questionnaire of the
ASU study within 48 days post-claim initiation (early
completers). Late completers were defined as those who
completed the baseline questionnaire more than 48 days
following the initiation of the claim. Furthermore, only
participants who completed the second follow-up questionnaire
between 130 and 230 days post-claim initiation
were included in the final cohort (Figure 1). These criteria
were necessary to ensure the baseline data clearly preceded
the collection of the outcome of interest and also optimize
statistical power by capturing 90 % of the sample.
The outcome was post-injury sustainable employment.
Workers with sustainable employment had no work
absence or an initial work absence followed by no future
absences post-injury. Workers without sustainable
employment were those who reported multiple episodes of
work absences and those who had not returned to work.
Sustainable employment at 1–month and 6–month postclaim
initiation was determined by asking study participants
the following questions:
 "Did you have to take time off work because of your back injury?" [
 "Have you returned to work?"
 "Between the time you returned to work and now (time of interview) did you have to take any additional time off work because of your back injury?"
Predictive factor selection was based on evidence provided
in the scientific literature and clinical judgment [9, 14–17].
These factors were then compared with those available in
the ASU dataset.
The final selected predictors included:
Age and sex.
Back and leg pain intensity
Numeric Rating Scales for
back pain and leg pain intensity (NRS-101), were used to
rate pain from "0" (not bothersome at all) to "100" (pain is
extremely bothersome). This scale is valid and reliable. 
Type of occupation
This was used as a basic clinical
predictor since certain work activities may have an impact
on recovery from back pain. Based on the ASU study
coding, workers were grouped into the classifications of (1)
professional/manager, (2) service, and (3) other.
The Roland-Morris Disability Scale (24 item)
was used to assess the functional abilities of individuals
with LBP. It has high internal consistency, test–retest
reliability (within a six-week period), criterion-based,
construct and discriminant validity, and responsiveness. [19–28]
Health related quality of life
The SF-12 (second revision)
was used to measure physical and mental healthrelated
quality of life. The questionnaire was administered
both at baseline and subsequent follow-up periods. This
scale has good test–retest reliability over a two-week period
and the SF-12 components are highly correlated with
the same components of the SF-36.  The SF-12 also has
good internal consistency, validity and responsiveness. 
Past history of LBP
Workers were asked if they had
LBP before this injury (yes/no).
Expectation of recovery
This was assessed by asking "Do you think that your LBP will get better soon?"
Available responses were:
(1) "already fully recovered";
(2) "get better soon";
(3) "get better slowly";
(4) "never get better"; or
(5) "get worse".
Workers were asked "Have you received
care from a health care provider for your back pain (yes/no)?"
This was determined by asking "Have
you hired an attorney’’ (yes/no)?" Litigation is directly related to the work injury and a result of the worker’s compensation claim.
This was calculated by subtracting the hire
date from the injury date and reported in years.
Workers were asked "How satisfied are
you with your job as a whole taking everything in consideration?"
Responses ranged from very satisfied to very
Participants were recruited from five separate
employers across the USA. To maintain anonymity of the
employers, they are referred to as Employer 1 through 5.
Elapsed time to interview
This was calculated by subtracting
the interview date from the injury date and
reported in days. Elapsed time to interview was grouped
within the work-related domain due to the varying reporting
procedures among employers.
Predictors were grouped into three different domains
using a hierarchical approach evolving from a basic clinical
assessment to a comprehensive review of factors collected
during an in-depth clinical encounter.
Description of Our Study Population
We examined the baseline characteristics of "early completers"
(n = 1,319) and "late completers" (n = 428) to
ensure that they were from the same underlying population
of workers with LBP. Differences between early and late
responders were measured with Chi square and t-tests.
Baseline characteristics of the final cohort and those lost to
follow-up were also examined to determine if they differed
Development of the Clinical Prediction Models
The models were built in three different stages. First, we
computed the correlations (Spearman) between predictors
to determine whether they were highly correlated. A correlation >0.7 was deemed too high for regression analysis,
in which case the most clinically relevant and easily
obtained (to minimize burden) predictor would be added to
the model. Next, we used bivariate logistic regression to
determine which predictors were associated with the outcome.
Predictors associated with the outcome (p <0.2 on
the Wald Chi square test) were considered in the third stage
of analysis. In the third stage, we built three sequential
logistic regression models to derive our final clinical prediction
model. This approach also allowed us to determine
the contribution of each domain of variables to the final
model. Separate prediction models were built for the
1–month and 6–month follow-up periods using a multistage
Model 1: Basic clinical predictors (age, sex, previous
episodes of back pain, intensity of back pain, intensity of
leg pain, and type of occupation).
Model 2: Health-related clinical predictors – Model 1
plus a more elaborate set of clinical predictors (functional
disability [RMDQ], health related quality of life
[SF-12], previous care for the injury, and recovery
Model 3: Work-related predictors – Model 2 plus
worker-related and job-related predictors (job tenure,
job satisfaction, claim litigation, who one’s employer is,
and elapsed time since baseline interview).
In Model 1, predictors associated with sustainable
employment (Wald Chi square p <0.1) were included.
Model 2 included all predictors from Model 1 plus more
elaborate health-related predictors associated with sustainable
employment (Wald Chi square p <0.1 using
backward selection). Model 3 included the predictors from
Model 2 plus work-related predictors associated with sustainable
employment (Wald Chi square p <0.1 using
backward selection). We assessed the presence of multicollinearity
by computing the variance inflation factor
(VIF); values exceeding 10 on the VIF indicated multicollinearity. 
The predictive accuracy of the models
was measured using a receiver-operating characteristic
curve (ROC) and the C-statistic. We interpreted our results
following the proposed guideline for interpreting ROC
curves: an AUC of ≥0.7 has acceptable discrimination,
≥0.8 is excellent, and ≥0.9 is outstanding.  This was
performed for all models derived using multivariable
logistic regression. All analyses were performed using SAS
software (SAS Institute, version 9.1, Carry, NC, USA).
Internal validity of the prediction models
We tested the
internal validity of the final prediction models using bias
corrected and accelerated confidence interval bootstrapping
(2000 repetitions). All bootstrapping analyses were performed
using Stata 9.2 (StataCorp, College Station, TX,
Of the 1,747 who made a workers' compensation claim for
back pain and completed the baseline survey, 1,319 completed
it within 48 days of the onset of their claim (early
completers) (Fig. 1). Compared to late responders, early
responders were less likely to report recovery, but they
were more likely to report favorable recovery expectations
(Table 1). Early responders, also reported worse back and
leg pain, functional limitations and health-related quality of
life. A comparison of baseline characteristics between the
final cohort (n = 736) and those who were lost to followup
(n = 583) suggests that those in the final cohort had
longer job tenure and were more likely to report previous
back pain (Table 2). A further comparison to assess the
effects of missing outcome data on baseline characteristics
among respondents who completed both follow-up surveys
did not identify any relevant differences between the
Patterns of Employment
Data was used only for individuals who answered all
return-to-work (RTW) questions at both follow-up periods
(n = 461). Seventy-nine percent (365/461) of the study
sample interviewed at the first follow-up had a sustainable
employment pattern. This decreased to 77 % (354/461) at
the second follow-up.
Prediction Models for the Final Cohort
Models from the 1–Month Follow-Up Period
The final clinical prediction model (Model 3) predicting
sustainable employment included back pain intensity, mental
health-related quality of life (SF-12 mental subscale),
claim litigation and type of employer (Table 3). The
predictive accuracy increased progressively across the
models from a c-index = 0.71 for Model 1 to a c-index
= 0.77 for Model 3. All three models had acceptable fit.
Models from the 6–Month Follow-Up Period
The final clinical prediction model (Model 3) for sustainable
employment at the 6–month follow-up included
physical and mental health-related quality of life (SF-12
physical and mental subscales), claim litigation and type of
employer (Table 4). The predictive accuracy increased
progressively across the models, from a c-index = 0.70 in
Model 1 to a c-index = 0.77 in Model 3 (Table 4). The
three models had acceptable fit.
The variance inflation factors tests indicated that no
collinearity was present in our models.
Our models are internally valid. Following bootstrapping,
the 95 % confidence intervals for the regression coefficients
in the models did not change.
To our knowledge, this is the first model available to
clinicians to assist with the prediction of sustainable
employment for occupational LBP based on a clinicallybased
conceptual framework. Furthermore, the ability to
accurately predict sustainable employment improved with
the addition of a few specific health- and work-related
factors. Our work supports the concept that LBP prognosis
is multifaceted and should include a variety of prognostic
factors from several domains.  Considering factors
such as health-related quality of life, claim litigation and
type of employer may help predict an individual’s work
status at both acute and sub-acute periods post-injury.
According to Hosmer and Lemeshow, our final models had
acceptable predictive accuracy (c-index = 0.77) at both
follow-ups.  Although the predictive accuracy of our
models is deemed acceptable, it could be improved.
Specifically, future models need to consider psychosocial
variables. Previous studies have illustrated the importance
of factors such as poor work relations, personality disorders,
and irritability/temper. [10, 15, 34]
Our clinical prediction model was conceptually developed
from the perspective of a clinician who treats patients
with back pain.Weasked two fundamental questions: "What
does a clinician ask when seeing a patient with a recent back
injury?" and "What information does the average clinician
typically collect to determine the prognosis of a patient with
a recent work-related back injury?". Our focus was to
identify subsets of key predictors that would be useful for
clinicians to identify whether a patient is likely to have a
sustainable employment pattern after his/her injury.
divided the factors considered in our models into three
1) demographic and basic clinical factors;
2) health-related factors; and
3) work-related factors.
clinical perspective, this approach was important to obtain a
comprehensive information base from injured workers.
Our study suggests that measuring specific clinical,
health-related and work-related variables (early after the
injury) may assist clinicians to predict sustainable
employment 1 month following the initiation of a claim for
back pain. Back pain intensity is easy to collect and already
part of a typical initial clinical encounter.
health-related and job-related factors, some of which can
be asked using standardized questionnaires and done during
history taking, include: mental health-related quality of
life (SF-12), claim litigation and type of employer. To
predict sustainable employment approximately 6–month
post-claim initiation, clinicians should examine physical
and mental health-related quality of life, claim litigation
and employer type. Of note is the similarity in predictors at
both follow-up periods; mental health-related quality of
life, litigation and employer type may impact sustainable
employment in both the short and long term post-injury.
Our findings also provide a valid method to inform injured
workers with back pain about their prognosis. This information
may be used to help educate, reassure and manage a
patient with an occupational back pain injury.
It is difficult to directly compare prediction models
because of methodological differences in the predictive
analytic methods, varying populations, and few consistently
measured prognostic factors. One clinical prediction
rule developed in a Quebec population used a similar
outcome; however, they used different predictors compared
to our study.  Variables included in the study by
Dionne et al. included: radiating pain, previous back surgery,
irritability/temper, sleep problems, and frequent
positional changes due to discomfort.  Their model,
which examined RTW two years after an occupational
injury, explained approximately 30 % of the variance.
However, the model had high negative predictive values
ranging from 74 to 91 %. Another study, conducted in
Ontario, identified a different set of predictors than our
models.  The study by McIntosh et al. used a datasplitting
technique to develop and test multivariable models.
Multivariable Cox proportional hazards were used to
predict duration on compensation benefits.
included the following variables:
(1) work in the construction industry;
(2) older age;
(3) elapsed time from injury to first treatment;
(4) pain referral into the leg;
(5) three or more Waddell signs;
(6) low back questionnaire score;
(7) previous episode of pain; and
(8) intermittent pain. The predictive accuracy in their confirmatory sample was 67.3 %.
The predictive accuracy of our clinical prediction
models is similar to previously developed clinical prediction
rules. A rule based on a biopsychosocial predictive
model for sub-acute and chronic LBP sufferers had a
similar predictive accuracy as our models for RTW. 
The biopsychosocial model correctly classified 80.5 % of
those returning to work and consisted of a number of
variables from the biopsychosocial spectrum including SF-
36 sub-scales, right leg sciatica, the perception of problem
severity and guarding on physical exam. A more recently
developed rule that examined risk prediction of prolonged
sick leave 6 months post-LBP injury contained job satisfaction,
fear avoidance beliefs, pain intensity, complaint
duration and sex.  Despite including factors from a
range of domains (clinical, work, psychosocial), the predictive
accuracy was moderate (c-index = 0.63) and the
explained variance was low. In another prediction rule, for
shoulder pain related to sick leave, satisfactory discrimination
was achieved (70 %).  Variables associated with
a higher risk of work absence 6 months post-initial consult
included: prolonged work absence prior to the initial consult,
higher shoulder pain intensity, overuse, and co-existing
Strengths and Limitations
Our study has several strengths. First, the models were
based on a large prospective incident cohort of workers
who made an insurance claim for work-related back pain.
The employees worked for five large national employers, in
both the private and public sectors, which encompassed a
fairly representative overview of the U.S. employed population.
One of the more innovative aspects of this dataset
was that it enabled us to measure the sustainability of
employment. Another distinct aspect of our work is that it
is clinically grounded. The three models used to create the
final prediction model were created from a clinician’s
perspective. Therefore, the predictors considered in our
models are clinically relevant.
Our study has some limitations. First, we could not
consider the full range of possible predictors because they
were not measured in the ASU study. These included comorbidities,
measures of social support, and the domain of
physical job demands. These have all been previously
reported as important predictors. [9–12, 34] Other variables
including race or ethnicity may also be relevant in a
biopsychosocial approach to managing back pain. 
Second, it is possible that selection bias due to non-participation
and attrition may have biased our results. Also,
depending on either clinical or theoretical perspective,
variables may be grouped under different domains than
what was done by the current authors. This may alter the
makeup of the final models presented. Given this is preliminary
work, others may feel it necessary to test these
models in other working populations while also regrouping
the variables. Furthermore, clinician judgement required in
choosing appropriate ICD-9 codes may lead to exclusion of
some cases. Finally, advances in occupational injury
management since the data sampling for this study are
likely. This may ultimately impact the outcome of the final
models if tested in other populations.
Although our study had limitations, our final models can
adequately predict who is likely to return to sustainable
employment following a back injury. Elapsed time between
the injury and the initial interview may have impacted the
predictive ability of the models. It is possible that some
variables may have become less predictive as more time
elapsed between the injury and the interview.
Our work suggests that employers could have important
implications for recovery. The results from this study may
have limited generalizability given the job sectors represented
and that only one employer was significant in the
final model at both follow-ups. Although the source population
included workers from 37 American states and
various industries, only five employers were used to derive
our sample. Even so, the results alert us to the potential that
there may be an employer effect on recovery patterns. This
may be related to specific employer support and policies
for managing injured workers. Other work with this cohort
has identified superior employment outcomes among
employers with more proactive RTW policies. 
Furthermore, it is important that researchers develop
predictions rules based on the principle of pragmatic
application – these rules are ultimately useless if the clinician
will not employ them in practice. To be adopted by
clinicians, clinical prediction rules must be simple to use
and of demonstrated additional benefit to patient outcomes.
The first level of evidence has been met, with the development
and initial validity testing of the models. Next steps
include testing these models in another distinct population
of workers and performing an impact analysis. Until then,
these models should not be used to direct patient management.
Also, we feel the predictive ability or our models
could be improved if additional factors are considered.
Future models could build on our conceptual framework
but further expand the multidimensional domains by
including variables examining co-morbidities, measures of
social support, and physical job demands.
Our analysis suggests that using information gathered
during the initial clinical encounter may assist health care
practitioners to better predict an injured worker’s post-back
injury employment pattern. We created a promising clinical
prediction model to predict sustainable employment
following a work-related back injury. Our models suggest
that clinicians might gain insight about sustainable
employment approximately 1 month after claim-initiation
by measuring back pain intensity, mental health-related
quality of life (SF-12), claim litigation and type of
employer. Similarly, examining physical and mental
health-related quality of life (SF-12), claim litigation, and
type of employer are adequate for predicting those with a
sustainable employment pattern approximately 6 months
This research was supported by the Graduate
Education and Research Department at the Canadian Memorial Chiropractic
College. The Arizona State University Healthy Back Study
was supported by a grant from the National Chiropractic Insurance
Company (NCMIC). Neither funding agency was involved in the
collection of data, data analysis, interpretation of data, or drafting of
Compliance with Ethical Standards
Conflict of Interest Dr. Shearer, Dr. Coˆte´, Dr. Frank, & Professor
Johnson declare they have no conflict of interest. Dr. Boyle has
received research grants payable to the University Health Network
from the Workplace Safety and Insurance Board and the Canadian
Chiropractic Protective Association. She has received a grant payable
to the University of Southern Denmark from the Fonden til fremme af
Kiropraktisk forskning og postgraduate uddannelse. Dr. Hayden has
received funding for a research professorship at Dalhousie University
from the Canadian Chiropractic Research Foundation.
The study protocol for the secondary analysis was
approved by the University Health Network and the University of
Toronto Research Ethics Boards. The ASU study protocol was
approved by the Institutional Review Boards at Arizona State
University and East Carolina University. All procedures performed in
studies involving human participants were in accordance with the
ethical standards of the institutional and/or national research committee
and with the 1964 Helsinki declaration and its later amendments
or comparable ethical standards.
Hoy DG, Smith E, Cross M, Sanchez-Riera L, Buchbinder R, Blyth
FM, et al.
The Global Burden of Musculoskeletal Conditions for 2010: An Overview of Methods
Ann Rheum Dis. 2014 (Jun); 73 (6): 982–989
Hong J, Reed C, Novick D, Happich M.
Costs associated with treatment of chronic low back pain: an analysis of the UK General Practice Research Database.
Cote P, Baldwin ML, Johnson WG, Frank JW, Butler RJ.
Patterns of sick-leave and health outcomes in injured workers with back pain.
Eur Spine J. 2008;17(4):484–93.
Bureau of Labor Statistics US Depatment of Labour.
Nonfatal occupational injuries and illnesses requiring days away from work, 2013.
Table 5. Number, incidence rate, and median days away from work for nonfatal occupational
injuries and illnesses involving days away from work by injury or illness characteristics
and ownership, 2013 2013
[cited 2014 December 16, 2014].
Mitchell RJ, Bates P.
Measuring health-related productivity loss.
Popul Health Manag. 2011;14(2):93–8.
Dunn KM, Jordan K, Croft PR.
Characterizing the Course of Low Back Pain: A Latent Class Analysis
American J Epidemiology 2006 (Apr 15); 163 (8): 754–761
Hestbaek L, Leboeuf-Yde C, Manniche C.
Low Back Pain: What Is The Long-term Course?
A Review of Studies of General Patient Populations
European Spine Journal 2003 (Apr); 12 (2): 149–165
Cassidy JD, Cote P, Carroll LJ, Kristman V.
Incidence and course of low back pain episodes in the general population.
Pransky GS, Verma SK, Okurowski L, Webster B.
Length of disability prognosis in acute occupational low back pain: development and testing of a practical approach.
Spine. 2006;31(6): 690–7.
Shaw WS, Pransky G, Patterson W, Winters T.
Early disability risk factors for low back pain assessed at outpatient occupational health clinics.
Steenstra IA, Koopman FS, Knol DL, Kat E, Bongers PM, de Vet HC, et al.
Prognostic factors for duration of sick leave due to lowback pain in Dutch health care professionals.
J Occup Rehabil. 2005;15(4):591–605.
Crook J, Milner R, Schultz IZ, Stringer B.
Determinants of occupational disability following a low back injury: a critical review of the literature.
J Occup Rehabil. 2002;12(4):277–95.
Hilfiker R, Bachmann LM, Heitz CA, Lorenz T, Joronen H, Klipstein A.
Value of predictive instruments to determine persisting restriction of function in patients with subacute nonspecific low back pain. Systematic review.
Eur Spine J. 2007;16(11):1755–75.
Cairns D, Mooney V, Crane P.
Spinal pain rehabilitation: inpatient and outpatient treatment results and development of predictors for outcome.
Dionne CE, Bourbonnais R, Fremont P, Rossignol M, Stock SR, Larocque I.
A clinical return-to-work rule for patients with back pain.
Hogg-Johnson S, Cole DC.
Early prognostic factors for duration on temporary total benefits in the first year among workers with
compensated occupational soft tissue injuries.
Occup Environ Med. 2003;60(4):244–53.
McIntosh G, Frank J, Hogg-Johnson S, Bombardier C, Hall H.
Prognostic factors for time receiving workers' compensation benefits in a cohort of patients with low back pain.
Jensen MP, Karoly P, Braver S.
The measurement of clinical pain intensity: a comparison of six methods.
Pain. 1986;27(1): 117–26.
Beurskens AJ, de Vet HC, Koke AJ.
Responsiveness of functional status in low back pain: a comparison of different instruments.
Deyo RA, Centor RM.
Assessing the responsiveness of functional scales to clinical change: an analogy to diagnostic test performance.
J Chronic Dis. 1986;39(11):897–906.
Deyo RA, Phillips WR.
Low back pain. A primary care challenge.
Hsieh CY, Phillips RB, Adams AH, Pope MH.
Functional outcomes of low back pain: comparison of four treatment groups in a randomized controlled trial.
J Manipulative Physiol Ther. 1992;15(1):4–9.
Kopec JA, Esdaile JM, Abrahamowicz M, Abenhaim L, Wood-Dauphinee S, et al.
The Quebec Back Pain Disability Scale. Measurement properties.
Leclaire R, Blier F, Fortin L, Proulx R.
A cross-sectional study comparing the Oswestry and Roland-Morris Functional Disability scales
in two populations of patients with low back pain of different levels of severity.
Patrick DL, Deyo RA, Atlas SJ, Singer DE, Chapin A, Keller RB.
Assessing health-related quality of life in patients with sciatica.
Roland M, Morris R.
A study of the natural history of back pain. Part I: development of a reliable and sensitive
measure of disability in low-back pain.
Stratford PW, Binkley J, Solomon P, Finch E, Gill C, Moreland J.
Defining the minimum level of detectable change for the Roland-Morris questionnaire.
Phys Ther. 1996;76(4):359–65.
Stratford PW, Binkley J, Solomon P, Gill C, Finch E.
Assessing change over time in patients with low back pain.
Phys Ther. 1994;74(6):528–33.
Ware J Jr, Kosinski M, Keller SD.
A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity.
Med Care. 1996;34(3):220–33.
Luo X, Lynn George M, Kakouras I, Edwards CL, Pietrobon R, et al.
Reliability, validity, and responsiveness of the short form 12-item survey (SF-12) in patients with back pain.
Cody RP, Smith JK.
Chapter 9: multiple regression analysis.
Applied Statistics and the SAS programming language. 5th ed.
Upper Saddle River, NJ: Pearson Prentice Hall; 2006. p. 282–319.
Hosmer DW, Lemeshow S.
Applied logistic regression. 2nd ed.
London: Wiley; 2000.
Hayden JA, Dunn KM, van der Windt DA, Shaw WS.
What is the prognosis of back pain?
Best Pract Res Clin Rheumatol. 2010;24(2):167–79.
Schultz IZ, Crook JM, Berkowitz J, Meloche GR, Milner R, Zuberbier OA, et al.
Biopsychosocial multivariate predictive model of occupational low back disability.
Heymans MW, Anema JR, van Buuren S, Knol DL, van Mechelen W, de Vet HC.
Return to work in a cohort of low back pain patients: development and validation of a clinical prediction
J Occup Rehabil. 2009;19(2):155–65.
Kuijpers T, van der Windt DA, van der Heijden GJ, Twisk JW, Vergouwe Y.
A prediction rule for shoulder pain related sick leave: a prospective cohort study.
BMC Musculoskelet Disord. 2006;7:97.
Turner JA, Franklin G, Fulton-Kehoe D, Sheppard L, Wickizer TM, Wu R, et al.
Worker recovery expectations and fear-avoidance predict work disability in a population-based workers'
compensation back pain sample.
Johnson WG, Butler RJ, Baldwin ML, Cote P.
Diasability risk management and postinjury employment patterns of workers with back pain.
Risk Manag Insur Rev. 2012;15(1):35–55.
Return to the RETURN TO WORK Section
Return to the CLINICAL PREDICTION RULE Page