Clinical classification criteria for nonspecific low back pain: A Delphi-survey of clinical experts

Dewitte, Vincent; Pauw, Robby De; Meulemeester, Kayleigh De; Peersman, Wim; Danneels, Lieven; Bouche, Katie; Roets, Arne; Cagnie, Barbara

doi:10.1016/j.msksp.2018.01.002

Cited by 30 publications

(26 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, it was not surprising to observe very high levels of agreement (96%) for elements pertaining to a specific mechanical pattern of pain . This observation is coherent with the results of a recent study by Dewitt and colleagues, which underlined a high level of agreement among clinicians regarding the association between nociceptive dysfunction patterns and specific patterns of pain as well as impaired neuromuscular control.…”

Section: Discussionsupporting

confidence: 90%

Operationalization of the new Pain and Disability Drivers Management model: A modified Delphi survey of multidisciplinary pain management experts

Tousignant-Laflamme

Cook

Mathieu

et al. 2019

Evaluation Clinical Practice

View full text Add to dashboard Cite

Background:We recently proposed the Pain and Disability Drivers Management (PDDM) model, which was designed to outline comprehensive factors driving pain and disability in low back pain (LBP). Although we have hypothesized and proposed 41 elements, which make up the model's five domains, we have yet to assess the external validity of the PDDM's elements by expert consensus. Research objectives:This study aimed to reach consensus among experts regarding the different elements that should be included in each domain of the PDDM model. Relevance:The PDDM may assist clinicians and researchers in the delivery of targeted care and ultimately enhance treatment outcomes in LBP. Methods:Using a modified Delphi survey, a two-round online questionnaire was administered to a group of experts in musculoskeletal pain management. Participants were asked to rate the relevance of each element proposed within the model. Participants were also invited to add and rate new elements. Consensus was defined by a greater than or equal to 75% level of agreement.Results: A total of 47 (round 1) and 33 (round 2) participants completed the survey. Following the first round, 38 of 41 of the former model elements reached consensus, and 10 new elements were proposed and later rated in the second round. Following this second round, consensus was reached for all elements (10 new + 3 from first round), generating a final model composed of 51 elements. Conclusion: This expert consensus-derived list of clinical elements related to the management of LBP represents a first step in the validation of the PDDM model.

show abstract

Section: Discussionsupporting

confidence: 90%

Operationalization of the new Pain and Disability Drivers Management model: A modified Delphi survey of multidisciplinary pain management experts

Tousignant-Laflamme

Cook

Mathieu

et al. 2019

Evaluation Clinical Practice

View full text Add to dashboard Cite

show abstract

“…Comparing the true‐positive subgroup to the false‐positive subgroup, we found that the false‐positive group was younger ( p < .001) and had lower NRS scores ( p < .001). We observed that false‐positive patients maintained a compulsive position with the trunk in a forward or lateral flexion posture and exhibited increased back muscle tension upon static spinal palpation. These clinical features may be evidence to differentiate non‐VFs from VFs.…”

Section: Discussionmentioning

confidence: 82%

Back Pain-Inducing Test, a Novel and Sensitive Screening Test for Painful Osteoporotic Vertebral Fractures: A Prospective Clinical Study

Jin

Liu

et al. 2019

Journal of Bone and Mineral Research

View full text Add to dashboard Cite

To detect painful vertebral fractures (VFs) in back pain populations at risk of osteoporosis, we designed a physical examination test (the Back Pain‐Inducing Test [BPIT]) that included three movements: lying supine, rolling over, and sitting up. If back pain is induced during any of these movements, the result is defined as positive, thereby establishing a presumptive diagnosis of painful VFs. Pain severity is quantified using a self‐reported numerical rating scale (NRS). The presence or absence of painful VFs is verified by whole‐spine magnetic resonance imaging (MRI), the gold standard for final diagnosis. According to the standards for reporting diagnostic accuracy, a real‐world, prospective, and observational study was performed on 510 back pain patients (enrolled from a single institute) at risk of osteoporosis. The sensitivity, specificity, and accuracy of the BPIT for identifying painful VFs were 99.1% (95% CI, 97.5% to 99.8%), 67.9% (95% CI, 60.4% to 74.5%), and 89.0%, respectively. The positive and negative predictive values were 86.6% (95% CI, 82.9% to 89.6%) and 97.4% (95% CI, 92.6% to 99.3%), respectively. Cutoff NRS scores for lying supine, rolling over, and sitting up were 3, 0, and 2, respectively. The corresponding area under the receiver operating characteristic curves (AUROCs) of each movement was 0.898 (95% CI, 0.868 to 0.922), 0.884 (95% CI, 0.854 to 0.911), and 0.910 (95% CI, 0.882 to 0.933), respectively. Although the high prevalence of VFs in the enrolled cohort partially limits the external validity of the predictive value in the general population, we conclude that the BPIT is potentially effective for detecting painful VFs in back pain populations at risk of osteoporosis. This test may be used as a stratification tool in decision‐making on subsequent imaging procedures: a negative BPIT rules out painful VFs and indicates that an MRI should be spared, whereas a positive BPIT means that an MRI is necessary and is likely to identify painful VFs. © 2019 American Society for Bone and Mineral Research.

show abstract

“…Two of the most commonly used methods for determining index weights are the Analytic Hierarchy Process (AHP) [24,25] and Delphi methods [26,27], which are highly subjective. So, this study used the entropy-weight method to determine the indicator weights.…”

Section: Entropy-weight Topsis Methodsmentioning

confidence: 99%

Performance Evaluation of Regional Water Environment Integrated Governance: Case Study from Henan Province, China

Tang

Dong

et al. 2020

IJERPH

View full text Add to dashboard Cite

The performance of the regional water environment integrated governance is affected by many factors. This study took place in Henan Province, China, as the research area, and constructed an index system through the comprehensive consideration of three target layers based on the Ecological-Social-Economic (ESE) framework. Due to advantages such as strong objectivity and operability, the improved entropy-weight technique for the order of preference by similarity to the ideal solution (TOPSIS) model can greatly overcome subjective human interference and render the evaluation results more reliable. Therefore, it was introduced to evaluate the water environment integrated governance in Henan from 2007 to 2016. By applying the obstacle degree model, the obstacle factors were then diagnosed. The results of this study show that the overall performance of the integrated governance was generally improved in Henan from 2007 to 2016. Performance levels of the three target layers exhibited different trends, of which the social and economic benefits presented a linear increase year by year, but the ecological benefits presented a fluctuating downward trend. The obstacle on the Henan water environment integrated governance mainly comes from the ecological and economic benefits index. Therefore, a series of countermeasures have been proposed as a means of improving the governance performance in Henan.the new era. To promote the construction of ecological civilization, the governance of environmental pollution must be accelerated as soon as possible. In this study, research into the issue of water environment integrated governance has gradually become a focal point. The formulation of a scientific and reasonable mechanism to evaluate the performance of water environment integrated governance is currently a significant issue in both the theory and practice of water environment improvement.The shortage and serious pollution of water resources are severe obstacles to meeting the demand for clean water [5,6]. Academic research on the water environment governance from different perspectives has been emerging in an endless stream [7]. At present, the research mainly includes the vulnerability of water resources [8], evaluation of water quality [9], and methods of urban water governance [10]. The construction of evaluation indexes has gradually changed from the traditional viewpoint of 'water ecological' to the more comprehensive evaluation of ecology, society and economics [11,12]. For example, Fekete and Stakhiv studied the establishment of such indicators for a performance evaluation index system, and outlined a conceptual framework for indicators suitable to guiding both the water management planning as well as implementation evaluation [13]. On the basis of the ILBM (Lake Basin Management Initiative), Chidammodzi and Muhandiki built an indicator system from two aspects, namely the water resource subsystems and socioeconomic subsystems for the evaluation of water environment governance in the Lake Malawi Basin [14].The existing rese...

show abstract

Clinical classification criteria for nonspecific low back pain: A Delphi-survey of clinical experts

Cited by 30 publications

References 85 publications

Operationalization of the new Pain and Disability Drivers Management model: A modified Delphi survey of multidisciplinary pain management experts

Operationalization of the new Pain and Disability Drivers Management model: A modified Delphi survey of multidisciplinary pain management experts

Back Pain-Inducing Test, a Novel and Sensitive Screening Test for Painful Osteoporotic Vertebral Fractures: A Prospective Clinical Study

Performance Evaluation of Regional Water Environment Integrated Governance: Case Study from Henan Province, China

Contact Info

Product

Resources

About