Clinical prediction rules

Chaitow, Leon

doi:10.1016/j.jbmt.2010.04.005

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…From the rules looking at back pain (8 rules in total involving 47 articles [ 77 – 121 , 150 , 152 ]), three rules (rules for manipulation and stabilization exercise, and the treatment-based classification system) have been the most commonly studied. Confirmatory evaluation of these rules has shown mixed or unsuccessful results.…”

Section: Resultsmentioning

confidence: 99%

Clinical Decision Support Tools for Selecting Interventions for Patients with Disabling Musculoskeletal Disorders: A Scoping Review

et al. 2015

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Purpose We aimed to identify and inventory clinical decision support (CDS) tools for helping front-line staff select interventions for patients with musculoskeletal (MSK) disorders. Methods We used Arksey and O’Malley’s scoping review framework which progresses through five stages: (1) identifying the research question; (2) identifying relevant studies; (3) selecting studies for analysis; (4) charting the data; and (5) collating, summarizing and reporting results. We considered computer-based, and other available tools, such as algorithms, care pathways, rules and models. Since this research crosses multiple disciplines, we searched health care, computing science and business databases. Results Our search resulted in 4605 manuscripts. Titles and abstracts were screened for relevance. The reliability of the screening process was high with an average percentage of agreement of 92.3 %. Of the located articles, 123 were considered relevant. Within this literature, there were 43 CDS tools located. These were classified into 3 main areas: computer-based tools/questionnaires (n = 8, 19 %), treatment algorithms/models (n = 14, 33 %), and clinical prediction rules/classification systems (n = 21, 49 %). Each of these areas and the associated evidence are described. The state of evidentiary support for CDS tools is still preliminary and lacks external validation, head-to-head comparisons, or evidence of generalizability across different populations and settings. Conclusions CDS tools, especially those employing rapidly advancing computer technologies, are under development and of potential interest to health care providers, case management organizations and funders of care. Based on the results of this scoping review, we conclude that these tools, models and systems should be subjected to further validation before they can be recommended for large-scale implementation for managing patients with MSK disorders.

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Section: Resultsmentioning

confidence: 99%

Clinical Decision Support Tools for Selecting Interventions for Patients with Disabling Musculoskeletal Disorders: A Scoping Review

et al. 2015

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“…These slings are, important in providing a whole-body appreciation of dysfunctional states, helping us to track back to where a problem may have arisen from and, indeed, as McNeill (2010) and Chaitow (2010) have discussed, to predict where a future problem may arise.…”

Section: Many Times In This Journal and In His Bookmentioning

confidence: 99%

Chains, trains and contractile fields

Wallden¹

2010

Journal of Bodywork and Movement Therapies

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“…One such result could be muscle strength impairments as a modifiable risk factor in patients with MNP. Chaitow's recent discussion of Clinical Prediction Rules e clinical examination findings that can predict the presence of a condition e may link muscle strength impairments in patients with MNP as a modifiable risk factor (Chaitow, 2010).…”

Section: Introductionmentioning

confidence: 99%