Literature DB >> 16942450

Clinical prediction rules: what are they and what do they tell us?

Paul Beattie1, Roger Nelson.   

Abstract

QUESTION: Clinical prediction rules are research-based tools that quantify the contributions of relevant patient characteristics to provide numeric indices that assist clinicians in making predictions. Clinical prediction rules have been used to describe the likelihood of the presence or absence of a condition, assist in determining patient prognosis, and help the classification of patients for treatment. The recent rapid rise in the use of clinical prediction rules raises questions about the conditions under which they may be used most appropriately. What is the potential role of clinical prediction rules in physiotherapy practice and what are the strategies by which clinicians can determine their appropriate use for a given clinical setting?
CONCLUSION: Clinical prediction rules use quantitative methods to build upon the body of literature and expert opinion and can provide quick and inexpensive estimates of probability. Clinical prediction rules can be of great value to assist clinical decision making but should not be used indiscriminately. They are not a replacement for clinical judgment and should complement rather than supplant clinical opinion and intuition. The development of valid clinical prediction rules should be a goal of physiotherapy research. Specific areas in need of attention include deriving and validating clinical prediction rules to screen patients for potentially serious conditions for which current tests lack adequate diagnostic accuracy or have unacceptable cost and risk, and to assist in classification of patients for treatments that are likely to result in substantially different outcomes in heterogeneous groups of patients.

Entities:  

Mesh:

Year:  2006        PMID: 16942450     DOI: 10.1016/s0004-9514(06)70024-1

Source DB:  PubMed          Journal:  Aust J Physiother        ISSN: 0004-9514


  28 in total

1.  Emotional-based practice.

Authors:  Chad Cook
Journal:  J Man Manip Ther       Date:  2011-05

Review 2.  Clinical prediction rules for physical therapy interventions: a systematic review.

Authors:  Jason M Beneciuk; Mark D Bishop; Steven Z George
Journal:  Phys Ther       Date:  2008-12-18

3.  Manual physical therapy in the Netherlands: reflecting on the past and planning for the future in an international perspective.

Authors:  Rob A B Oostendorp
Journal:  J Man Manip Ther       Date:  2007

4.  A new model for orthopaedic manual therapy research: description and implications.

Authors:  Peter A Huijbregts
Journal:  J Man Manip Ther       Date:  2007

5.  Potential pitfalls of clinical prediction rules.

Authors:  Chad E Cook
Journal:  J Man Manip Ther       Date:  2008

6.  Thoracic manual therapy in the management of non-specific shoulder pain: a systematic review.

Authors:  Aimie L Peek; Caroline Miller; Nicola R Heneghan
Journal:  J Man Manip Ther       Date:  2015-09

7.  Research methods for subgrouping low back pain.

Authors:  Peter Kent; Jennifer L Keating; Charlotte Leboeuf-Yde
Journal:  BMC Med Res Methodol       Date:  2010-07-03       Impact factor: 4.615

8.  Effectiveness of mechanical diagnosis and therapy in patients with back pain who meet a clinical prediction rule for spinal manipulation.

Authors:  Ron Schenk; Carol Dionne; Corey Simon; Robert Johnson
Journal:  J Man Manip Ther       Date:  2012-02

9.  Independent evaluation of a clinical prediction rule for spinal manipulative therapy: a randomised controlled trial.

Authors:  Mark J Hancock; Christopher G Maher; Jane Latimer; Robert D Herbert; James H McAuley
Journal:  Eur Spine J       Date:  2008-04-22       Impact factor: 3.134

10.  An empiric risk scoring tool for identifying high-risk heterosexual HIV-1-serodiscordant couples for targeted HIV-1 prevention.

Authors:  Erin M Kahle; James P Hughes; Jairam R Lingappa; Grace John-Stewart; Connie Celum; Edith Nakku-Joloba; Stella Njuguna; Nelly Mugo; Elizabeth Bukusi; Rachel Manongi; Jared M Baeten
Journal:  J Acquir Immune Defic Syndr       Date:  2013-03-01       Impact factor: 3.731

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