Spine (Phila Pa 1976). 2019 (Nov 15); 44 (22): E1298–E1310 ~ FULL TEXT
Birgitte Lawaetz Myhrvold, MSc, Alice Kongsted, PhD, Pernille Irgens, MSc,
Hilde Stendal Robinson, PhD, Magne Thoresen, PhD, and Nina Køpke Vøllestad, PhD
Department of Interdisciplinary Health Sciences,
Institute of Health and Society,
University of Oslo,
STUDY DESIGN:   A prospective observational study.
To externally validate the prediction model developed by Schellingerhout and colleagues predicting global perceived effect at 12 weeks in patients with neck pain and to update and internally validate the updated model.
SUMMARY OF BACKGROUND DATA:   Only one prediction model for neck pain has undergone some external validation with good promise. However, the model needs testing in other populations before implementation in clinical practice.
METHODS:   Patients with neck pain (n = 773) consulting Norwegian chiropractors were followed for 12 weeks. Parameters from the original prediction model were applied to this sample for external validation. Subsequently, two random samples were drawn from the full study sample. One sample (n = 436) was used to update the model; by recalibration, removing noninformative covariates, and adding new possible predictors. The updated model was tested in the other sample (n = 303) using stepwise logistic regression analysis. Main outcomes for performance of models were discrimination and calibration plots.
RESULTS:   Three hundred seventy patients (47%) in the full study sample reported persistent pain at 12 weeks. The performance of the original model was poor, area under the receiver operating characteristics curve was 0.55 with a Confidence Interval of 0.51-0.59. The updated model included Radiating pain to shoulder and/or elbow, education level, physical activity, consultation-type (first- time, follow-up or maintenance consultation), expected course of neck pain, previous course of neck pain, number of pain sites, and the interaction term Physical activity##Number of pain sites. The area under the receiver operating characteristics curve was 0.65 with a 95% Confidence Interval of 0.58-0.71 for the updated model.
CONCLUSION:   The predictive accuracy of the original model performed insufficiently in the sample of patients from Norwegian chiropractors and the model is therefore not recommended for that setting. Only one predictor from the original model was retained in the updated model, which demonstrated reasonable good performance predicting outcome at 12 weeks. Before considering clinical use, a new external validation is required.
KEYWORDS:   chiropractic, external validation, neck pain, performance accuracy, prediction rule, prognosis, updating.
LEVEL OF EVIDENCE:   3.
From the FULL TEXT Article:
Neck pain is common with an annual prevalence of
30% to 50% in the general population, [1–5] and
causes substantial disability and economic
expenses. [1, 3, 6–8] Since the highest burden of disability and
health expenses, relate to a small proportion of those with
neck pain there is a need to identify factors that can predict
patients’ likely outcome and tailor management accordingly. Although a number of predictors associated with neck
pain have been identified, evidence related to prediction of
the clinical course of recovery and decisions regarding
choice of treatment is limited and inconsistent. [6, 9, 10] A recent
literature review concluded that the quality of studies developing prediction models on neck pain varies, and that the
majority of models lack proper validation.  In 2010, Schellingerhout et al  identified predictive factors and developed
a prediction model aiming at identifying patients at risk of
persistent neck pain complaints after 6 months and confirmed its validity in a separate patient sample (external
validation). To our knowledge, this is the only prediction
model for neck pain that has been externally validated.
However, the model has not been externally validated in
settings other than general practice and physiotherapy in
England and the Netherlands. This may be important as
patient populations and predictors may differ across countries and settings. [13, 14]
The aims of this study were therefore to (1) externally
validate the prediction model developed by Schellingerhout
(hereafter "the original model") in patients with neck pain
consulting Norwegian chiropractors, (2) recalibrate the
original model in chiropractic neck patients, (3) potentially
update the original model by adding new predictors, and (4)
internally validate the updated model.
MATERIALS AND METHODS
This study was part of a 1-year observational study following patients with neck pain consulting chiropractic practice.
Decisions regarding treatment were at the discretion of the
individual chiropractor, irrespective of study participation.
Altogether 71 members of the Norwegian Chiropractic
Association agreed to recruit patients. They were located
across all parts of Norway, reflecting urban and rural areas.
Patients can be either referred or self-referred to chiropractic
treatment and qualify for partial refund from the Norwegian
health care system. 
Patients aged 18 to 70 years, presenting with neck pain as a
primary or secondary complaint with or without arm pain
were invited to participate. They were eligible for inclusion
regardless of pain duration and if they had started treatment
or not. Participants should be able to read and write Norwegian, and to respond SMS messages on a mobile phone
(not used in this study). Exclusion criteria were suspicion of
serious pathology or fracture as cause of neck pain. Chiropractors were instructed to invite all consecutive patients
presenting with neck pain. The inclusion period was September 2015 to May 2016.
All patients received oral and written information about the
study from the chiropractor. All participants signed a written informed consent. The participants received questionnaires on paper or electronically. Paper questionnaires were
given at recruitment by the chiropractor, and returned by
the participant in a pre-paid envelope to the researchers.
Participants choosing electronic questionnaires received an
e-mail within 2 days with a link to the baseline questionnaire. Follow-up questionnaires were sent after 12 weeks.
Those not responding within 7 days had one written
reminder followed by a phone call 2 weeks later.
Patient Reported Baseline Variables
The baseline questionnaires included all nine predictors
from the original model of Schellingerhout et al  (Table 1). In addition, demographic variables and other
potential predictors used to update the model were selected
based on the literature, [10, 23–25] recommendations on prediction model development [26, 27] and clinical experience. Hence,
we included sum scores from five questionnaires. [16–21] Two
of the additional variables were visual trajectory patterns
describing the course of neck pain in the past year and
expected course of neck pain in the forthcoming year. Five
trajectories were made based on the literature of trajectory
patterns [25, 28] (Figure 1). All potential predictors were
divided into five domains to be used when updating the
model because many of the included potential predictors
may carry similar or overlapping information (Table 1).
Consultation-type describes when in the course of treatment
participants were recruited ("First-time consultation" = recruited at the first visit for a new episode of neck
pain, "Follow-up consultation" = recruited during a clinical
course of treatment, "Maintenance consultation" = patients
visiting the chiropractor regularly at pre-planned time
The outcome measure was self-reported global perceived
effect (GPE) at 12 weeks.  GPE was measured on a 7-point
Likert-scale (0 = "completely recovered" to 6 = "worse
than ever"). Scores of 2–6 were coded as "persistent complaints" and used as outcome for the analysis as in the
original model. 
The Data Sets and Approach for Validation and Update
First, the full study sample (n = 773) was used for external
validation of the original model. Second, the full study
sample was used for a recalibration of the original model.
Third, the original model was updated using a randomly
created split-sample from the full study sample stratified by
number of recruited patients per clinic to achieve equal
representation of clinics as in the full study sample (Development sample, n = 436). Fourth, the updated model was
tested in the rest of the full study sample (Validation sample,
n = 307), (Figures 2 and 3).
Descriptive analyses are presented as frequencies (%)
or mean values with standard deviation (SD). Univariate
logistic regression analysis was used to estimate relationships between the outcome and the variables Consultation-type and Duration current episode as well as their
potential moderation of the relationship between original
predictors and outcome. Only complete-cases where
analyzed, thus we excluded 46 individuals having one
or more single items missing of the predictors. All
analyses were carried out using Stata version 15 (StataCorp., TX).
Stage 1. Independent External Validation of the Original Model
The original model  was applied in our full study sample
(n= 773) using fixed coefficients, that is, by transporting the
coefficients from the original model to the validation setting
(Table 2). The validity of the model was evaluated in terms of
calibration and discrimination. [20, 31, 32] Calibration was assessed
graphically by the agreement between predicted and observed
outcomes. The Hosmer-Lemeshow goodness-of-fit test is testing the null-hypothesis that observed and predicted outcomes
do not differ.  Discrimination was assessed by area under the
receiver-operating characteristic curve (AUC).
Stage 2. Recalibration of the Original Model
Recalibration of the original model coefficients (intercept
and slope) was performed in the full study sample. The
regression coefficients of the recalibrated model were evaluated as in Stage 1.
Stage 3. Update of the Original Model in the Development Sample
The original model was updated using the development
sample (n= 436). The update included recalibration of the
coefficients of the predictors and removing/adding new predictors by a non-automated criterion-based procedure. First,
original predictors were removed if the model fit was not
significantly impaired (tested by the Likelihood Ratio test
[LR] and Akaike’s information criterion [AIC]). Starting with
the interaction terms and followed by the individual predictors we removed those with an OR closest to one and with the
largest P-values as long as the AIC value or LR were not
negatively affected. The updated model with the lowest AIC
value and an unchanging LR was chosen. Within the five
domains described in Table 1, all potential predictors were
included in logistic regression models using GPE as outcome
and removed one by one based on AIC and LR.
The best performing predictor(s) from each domain were
then included in the updated model. Finally, predictors were
removed from the updated model using the same nonautomated procedure as for updating the original model.
The performance of the updated model was evaluated in
terms of calibration and discrimination. The non-automatic
approach was chosen to avoid unstable variable selection
from stepwise methods.  A sufficiently large sample size
provided more stable estimates of model performance. [35–37]
Stage 4. Internal Validation of the Updated Model
Reproducibility of the updated model obtained from Stage 3
was tested using the Validation sample (n = 307), (internal
validation). The updated model was evaluated in terms of
calibration and discrimination as in Stage 1.
The study was approved by Regional committees for
medical and health research ethics (2015/89).
Altogether 1,478 patients were recruited of which 1102 met
the inclusion criteria and were included in this study. Dropouts were 28% (n = 313) for outcome measures at 12-week
follow-up. Hence, the full study sample consisted of 773
patients (Figure 2). The baseline characteristics of our full
study sample, dropouts and the original study are presented
in Table 3. Participants included in the analyses and those
lost to follow-up had similar sociodemographic and neck
pain related variables. Participants of the original study had
a higher fraction of male participants (39% vs. 25%), a
lower education level, a smaller fraction reporting previous
neck complaints (64% vs. 87%) and a lower prevalence of
low back pain (21% and 55%). Persistent complaints at
12 weeks in our study was 47% compared with 43% after
6 months in the original study.
Stage 1. Independent External Validation of the Original Model
The original model showed poor discriminative ability:
AUC (95% CI) = 0.55 (0.51–0.59) (Figure 4) and the calibration plot showed a poor fit of the model to the data
(Figure 5) (Hosmer-Lemeshow test P < 0.001). Key results
of validation and updating during all stages are shown in
Table 4, Figures 4 and 5.
Stage 2. Recalibration of the Original Model
The performance improved after recalibration of the original model; see Tables 2 and 4 for details. The AUC increased
(Figure 4) and the calibration plot showed a clear improvement in precision (Figure 5). Pain intensity and radiating
pain showed a stronger association with outcome in our
sample as compared with the original study, whereas low
back pain was less predictive. None of the interaction terms
from the original model were significantly associated with
outcome (Table 2).
Stage 3. Update of the Original Model in the Development Sample
The updated model included seven predictors and one
interaction term; Table 1 shows excluded and included
predictors of the updated model and Table 5 show parameters of the updated model. The updated model included
radiating pain to shoulder and/or elbow, education level,
physical activity, consultation-type, expected course of neck
pain, previous course of neck pain, number of pain sites, and
the interaction term physical activity##number of pain sites.
The only original predictor remaining in the updated model
was radiating pain. The performance of the updated model
improved compared with the original and to the recalibrated
model with respect to both discriminative ability and calibration (Figures 4 and 5).
Stage 4. Internal Validation of the Updated Model
The updated model had reasonable discriminative ability:
AUC (95% CI) = 0.65 (0.58–0.71) in the validation sample
(n = 307) see Figure 4. Calibration plot predicted best those
at low-risk of persistent pain (Figure 5) but the HosmerLemeshow test was significant (P < 0.01).
Our study showed poor external validity of the original
model in Norwegian chiropractic patients with neck pain.
Performance was improved through recalibration of the
model. However, the model was still not able to predict
GPE well in this setting. During the update, all predictors
from the original model apart from radiating pain were
excluded and replaced with new ones resulting in a model so
different from the original one that new external validation
The original model had previously been externally validated with a reasonable discrimination and an acceptable
calibration.12 Our study was a large prospective cohort
study planned for the external validation with all original
predictors and outcome collected and categorized similar to
the original study. Some methodological differences might
be of importance. Outcome was measured after 12 weeks
rather than 6 months in the original study. Still, this disparity did not give any major difference in the prevalence of the
outcome, 47% versus 43%. As most substantial improvements occur within 6–12 weeks after care seeking, [38–40] it is
expected that a model, being predictive of 6-months outcome, would also predict 3-months outcomes. We only
included complete cases instead of imputing missing
data. [27, 41] This may have introduced bias, but with a small
number of missing items (<3%) we believe this to be of
The lack of external validity in our study could be due to
differences between the two samples, indicated by different
distributions of predictors between the populations. The
original model was developed using patients participating in
a RCT, but applied in our study sample with looser inclusion
criteria. Different populations might also be expected due to
the settings; the original model developed in general practice
and physiotherapy and our validation in chiropractic setting. The two cohorts differed on sex, education, previous
complaints, and presence of low back pain, which are all
factors known to be associated with persistent neck pain.10
Furthermore, less restricted criteria resulted in twice as
many participants with pain duration more than 3 months
compared with the original model. This difference may
affect prediction since a longer duration of the symptoms
at baseline is related to poor outcome. [42, 43] However, the
associations between predictors and outcome were not
substantially different between groups differing in duration
and we believe the longer pain duration explains only little
of the poor performance. It is also possible that there were
differences in psychosocial factors between the samples,
although the lack of such data in the original study precludes
further discussion of this.
The present model update included three novel predictors
that seemed to be stronger than some of the predictors
commonly used. Previous and Expected visual trajectory
patterns both showed strong association with outcome. This
may indicate that patients’ recall of their overall symptom
history may carry more prognostic information than traditional measures of previous episodes and episode duration.  This fits well with emerging evidence on quite
stable long-term trajectories of spinal pain. [44, 45] Inclusion
of the predictor Expected course of neck pain in the updated
model should possibly be interpreted as a proxy measure of
patients’ expectations and possible psychological distress.25
It is somewhat surprising that this variable was a stronger
predictor than the traditional variables reflecting psychosocial factors. These findings need further investigation
regarding prognostic information of the trajectory patterns.
It is also noteworthy that the variable Consultation-type
had prognostic effect. Patients receiving maintenance care
have poorer outcome after 3 months compared with those
included with a new episode. This is not surprising, but
suggests that this variable includes information of prognostic value that complements the other variables. Further
investigation is needed to understand how this variable
affects outcome and whether similar effects can be found
for patients in other settings.
The inclusion of the novel predictors provided a model
that performed best in identifying people with a low probability of persistent pain. This will be of value in reducing
overtreatment of patients with a good prognosis. The
performance of the model for identifying persons with high
probability of persistent pain was less informative. The
significant Hosmer-Lemeshow test should be interpreted
with caution because a large sample size increases the
probability of a statistically significant lack of fit. The
updated model should thus be further evaluated and not
rejected solely based on this test.
Our results raise the question whether it is realistic that
prediction models from one setting have the potential for
use in other settings. In acute low back pain, promising
results imply that the same model in Australian general
practice and Danish chiropractic can identify people with a
good outcome of care.  This does not preclude, however,
that even better prognosis could be obtained in these
settings with another model. One might even hypothesise
that models for this kind of health service with a wide
set of treatment options, should be individualized to
This study is one of few to independently externally
validate a prediction model for neck pain.  We did not
find that the original model was predictive in this sample
of patients managed by chiropractors. The betweenpopulation-heterogeneity might be a limitation when transferring prediction models to different settings. An attempt to
update the model resulted in a new prediction model that
was able to predict patients with a favorable outcome. It is,
however still pre-mature to be used in clinical decisionguidance and would need further evaluation and perhaps
updating before implementation is considered.
A previously validated prediction model (called original model) was externally validated in 773 chiropractic patients with neck pain.
The performance of the external validation of the original model was poor.
An update of the original model included the predictors: radiating pain to shoulder and/or elbow, education level, physical activity, consultation-type, expected course of neck pain, previous course of neck pain, number of pain sites, and the interaction term physical activity##number of pain sites.
The updated prediction model performed best in identifying people with a low probability of persistent pain but need further evaluation.
The updated prediction model included patientreported visual trajectories of neck pain pattern that should be investigated further regarding prognostic information.
The authors thank Knut Waagan for his help during statistical analyses. They also thank the participating chiropractors and patients.
Hogg-Johnson, S, van der Velde, G, Carroll, LJ et al.
The Burden and Determinants of Neck Pain in the General Population:
Results of the Bone and Joint Decade 2000–2010 Task Force
on Neck Pain and Its Associated Disorders
Spine (Phila Pa 1976). 2008 (Feb 15); 33 (4 Suppl): S39–51
Fejer R, Kyvik KO, Hartvigsen J.
The prevalence of neck pain in the world population: a systematic critical review of the literature.
Eur Spine J 2006;15:834–48.
Hoy DG, et al.
The epidemiology of neck pain.
Best Pract Res Clin Rheumatol 2010;24:783–92.
Vos T, Flaxman AD, Naghavi M, et al.
Years Lived with Disability (YLDs) for 1160 Sequelae of 289 Diseases and Injuries
1990-2010: A Systematic Analysis for the Global Burden of Disease Study 2010
Lancet. 2012 (Dec 15); 380 (9859): 2163–2196
Natvig B, et al.
Musculoskeletal symptoms in a local community.
Eur J Gen Pract 1995;1:25–8.
Graham N, et al.
An ICON overview on physical modalities for neck pain and associated disorders.
Open Orthop J 2013;7: 440–60.
DALYs GBD, Collaborators H.
Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.
Lærum E, Brage S, Ihlebæk C, Johnsen K, Natvig B, & Aas E.
A musculoskeletal accounting: Prevalence and costs related to injuries, disease and disorders of the musculoskeletal system: MSTreport.
1/2013. MST-report. ISBN 978-82-303-2311-3 ed:
The Norwegian council for musculoskeletal health by/ FORMI, Division of surgery and neuroscience,
Oslo university hospital - Ulleva?l.
Postbox 4956 Nydalen, 0424 Oslo, Norway 2013:21–3.
Hurwitz, EL, Carragee, EJ, van der Velde, G et al.
Treatment of Neck Pain: Noninvasive Interventions: Results of the Bone and Joint Decade
2000–2010 Task Force on Neck Pain and Its Associated Disorders
Spine (Phila Pa 1976). 2008 (Feb 15); 33 (4 Suppl): S123–152
Carroll LJ, Hogg-Johnson S, van der Velde G, Haldeman S, Holm LW, Carragee EJ, Hurwitz EL.
Course and Prognostic Factors for Neck Pain in the General Population: Results of the Bone and Joint Decade
2000–2010 Task Force on Neck Pain and Its Associated Disorders
Spine (Phila Pa 1976). 2008 (Feb 15); 33 (4 Suppl): S83–92
Kelly J, Ritchie C, Sterling M.
Clinical prediction rules for prognosis and treatment prescription in neck pain: a systematic review.
Musculoskelet Sci Pract 2017;27:155–64.
Schellingerhout JM, et al.
Prognosis of patients with nonspecific neck pain: development and external validation of a prediction rule for persistence of complaints.
Spine (Phila Pa 1976) 2010;35: E827–35.
Hestbaek L, Munck A, Hartvigsen L, Jarbol DE, Sondergaard J, Kongsted A.
Low Back Pain in Primary Care: A Description of 1250 Patients with Low Back Pain
in Danish General and Chiropractic Practice
Int J Family Med. 2014 (Nov 4); 2014: 106102
Morso L, et al.
The predictive and external validity of the STarT Back Tool in Danish primary care.
Eur Spine J 2013;22:1859–67.
Legislation regarding changes in treatment reimbursement for chiropractors and manual therapists, the ministry of health and care services: proposition no 28 (Ot. prp. nr. 28), 2011.]. Available
Accessed June 29, 2019.
Derogatis LR, et al.
The Hopkins Symptom Checklist (HSCL): a self-report symptom inventory.
Behav Sci 1974;19:1–15.
Linton SJ, Boersma K.
Early identification of patients at risk of developing a persistent back problem: the predictive validity of the Orebro Musculoskeletal Pain Questionnaire.
Clin J Pain 2003;19:80–6.
Grotle M, Vollestad NK, Brox JI.
Screening for yellow flags in first-time acute low back pain: reliability and validity of a Norwegian version of the Acute Low Back Pain Screening Questionnaire.
Clin J Pain 2006;22:458–67.
Kuorinka I, et al.
Standardised Nordic questionnaires for the analysis of musculoskeletal symptoms.
Appl Ergon 1987;18: 233–7.
Von Korff M, Jensen MP, Karoly P.
Assessing global pain severity by self-report in clinical and health services research.
Spine (Phila Pa 1976) 2000;25:3140–51.
Vernon H, Mior S.
The Neck Disability Index: A Study of Reliability and Validity
J Manipulative Physiol Ther 1991 (Sep); 14 (7): 409–415
EuroQol–a new facility for the measurement of healthrelated quality of life.
Health Policy 1990;16:199–208.
Kongsted A, Kent P, Axen I, Downie AS, Dunn KM.
What Have We Learned From Ten Years of Trajectory Research in Low Back Pain?
BMC Musculoskelet Disord. 2016 (May 21); 17 (1): 220
Dunn KM, Jordan K, Croft PR.
Characterizing the course of low backpain: a latent class analysis.
Am J Epidemiol 2006;163:754–61.
Dunn KM, Campbell P, Jordan KP.
Validity of the visual trajectories questionnaire for pain.
J Pain 2017;18:1451–8.
Clinical prediction models. A practical approach to development, validtion, and updatinged.
Springer ScienceRBusiness Media, LLC,
233 Spring Street,
New York, NY 10013, USA:
Clinical Prediction Models. A Practical Approach to Development, Validtion, and Updating. 2009:
Kongsted A, Hestbaek L, Kent P.
How Can Latent Trajectories of Back Pain be Translated into Defined Subgroups?
BMC Musculoskelet Disord. 2017 (Jul 3); 18 (1): 285
Myburgh C, Brandborg-Olsen D, Albert H, Hestbaek L.
The Nordic Maintenance Care Program: What Is Maintenance Care?
Interview Based Survey of Danish Chiropractors
Chiropractic & Manual Therapies 2013 (Aug 20); 21: 27
Kamper SJ, et al.
Global Perceived Effect scales provided reliable assessments of health transition in people with musculoskeletal disorders, but ratings are strongly influenced by current status.
J Clin Epidemiol 2010;63:760.e1–6.e1.
Steyerberg EW, et al.
Assessing the performance of prediction models: a framework for traditional and novel measures.
Moons KM, et al.
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod): explanation and elaboration.
Ann Int Med 2015;162:W1–73.
Hosmer DW Jr, Lemeshow S.
Applied Logistic Regression, Second edition ed.
New York: Wiley-Interscience Publication; 2000.
Sun GW, Shook TL, Kay GL.
Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis.
J Clin Epidemiol 1996;49:907–16.
Kent P, et al.
Four hundred or more participants needed for stable contingency table estimates of clinical prediction rule performance.
J Clin Epidemiol 2017;82:137–48.
Collins GS, Ogundimu EO, Altman DG.
Sample size considerations for the external validation of a multivariable prognostic model: a resampling study.
Stat Med 2016;35:214–26.
Vergouwe Y, et al.
Substantial effective sample sizes were required for external validation studies of predictive logistic regression models.
J Clin Epidemiol 2005;58:475–83.
Leaver AM, et al.
People seeking treatment for a new episode of neck pain typically have rapid improvement in symptoms: an observational study.
J Physiother 2013;59:31–7.
Walton DM, et al.
Exploring the clinical course of neck pain in physical therapy: a longitudinal study.
Arch Phys Med Rehabil 2014;95:303–8.
Vasseljen O, et al.
Natural course of acute neck and low back pain in the general population: the HUNT study.
Pain 2013;154: 1237–44.
Janssen KJ, et al.
Missing covariate data in medical research: to impute is better than to ignore.
J Clin Epidemiol 2010;63:721–7.
Bot SD, et al.
Predictors of outcome in neck and shoulder symptoms: a cohort study in general practice.
Spine (Phila Pa 1976) 2005;30:E459–70.
Bruls VE, Bastiaenen CH, de Bie RA.
Prognostic factors of complaints of arm, neck, and/or shoulder: a systematic review of prospective cohort studies.
Dunn, K.M., Campbell, P., and Jordan, K.P.
Long-term Trajectories of Back Pain: Cohort Study With 7-year Follow-up
BMJ Open. 2013 (Dec 11); 3 (12): e003838
Lemeunier N, Leboeuf-Yde C, Gagey O.
The natural course of low back pain: a systematic critical literature review.
Chiropr Man Therap 2012;20:33.
da Silva T, Macaskill P, Kongsted A, et al.
Predicting pain recovery in patients with acute low back pain: Updating and validation of a clinical prediction model.
Eur J Pain 2019;23:341–53.
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