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      Clinicians’ heuristic assessments of radiographs compared with Kellgren-Lawrence and Ahlbäck ordinal grading: an exploratory study of knee radiographs using paired comparisons

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          Abstract

          Objectives

          Ordinal scales provide means for communicating the severity of a condition, but they are affected by cognitive biases, they introduce statistical problems and they sacrifice resolution. Clinicians discern more details than contained in scales, for example, when assessing radiographs, but clinicians’ distinctions are often based on experience-based rules of thumb, that is, heuristics. The objectives of this study are to compare clinicians’ heuristic assessments to ordinal grading, to identify case elements that influence clinicians’ judgements and to present a method for quantifying heuristic assessments.

          Design

          Clinicians were presented with 17 207 random pairs from a set of 1087 knee radiographs. For each pair, the radiograph with more severe osteoarthritis was selected. The Bradley-Terry model was used to calculate an osteoarthritis strength parameter for each radiograph. Similarly, strength parameters were determined for 12 morphological features with five additional features being considered either present or absent. All radiographs were also graded according to conventional ordinal systems (Kellgren-Lawrence and Ahlbäck). Relations between clinicians’ judgements and (1) the heuristics-based osteoarthritis strength, (2) conventional ordinal systems and (3) morphological features were investigated.

          Results

          Receiver operating characteristic analysis showed that the Bradley-Terry model provided a good description of clinicians’ assessments (area under the curve (AUC)=0.97, 95% CI 0.968 to 0.972). Morphological features (AUC=0.90, 95% CI 0.900 to 0.908) provided a superior description of clinicians’ choices compared with conventional ordinal systems (AUC=0.88, 95% CI 0.878 to 0.887 and AUC=0.80, 95% CI 0.796 to 0.809) for Ahlbäck and Kellgren-Lawrence, respectively). The features most strongly associated with osteoarthritis strength were medial joint space width, flattening of the medial femoral and tibial condyles, medial osteophytes and alignment.

          Conclusions

          Heuristics-based assessments give a better distinction than conventional grading systems of knee osteoarthritis. The example presents a general approach to evaluate which features are part of experts’ heuristics. The data suggest that experts discern more details than included in conventional ordinal grading systems. Quantitative heuristic assessments may replace ordinal scales.

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          Most cited references32

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          Judgment under Uncertainty: Heuristics and Biases.

          This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.
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            Radiological assessment of osteo-arthrosis.

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              • Record: found
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              • Article: not found

              Classifications in Brief: Kellgren-Lawrence Classification of Osteoarthritis.

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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2021
                8 March 2021
                : 11
                : 3
                : e041793
                Affiliations
                [1 ]departmentSection of Biostatistics , University of Copenhagen , Copenhagen, Denmark
                [2 ]departmentDepartment of Radiology , Copenhagen University Hospital Herlev-Gentofte , Copenhagen, Denmark
                [3 ]departmentDepartment of Orthopedic Surgery , Copenhagen University Hospital Herlev-Gentofte , Copenhagen, Denmark
                [4 ]departmentDepartment of Orthopedic Surgery, Rigshospitalet - Copenhagen , University Hospital , Copenhagen, Denmark
                [5 ]departmentDepartment of Clinical Medicine , University of Copenhagen , Copenhagen, Denmark
                Author notes
                [Correspondence to ] Anders Odgaard; anders.odgaard@ 123456regionh.dk
                Article
                bmjopen-2020-041793
                10.1136/bmjopen-2020-041793
                7942253
                e0eb77d5-9fa2-4a26-86aa-aacd4aefe649
                © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 17 June 2020
                : 23 December 2020
                : 21 January 2021
                Categories
                Research Methods
                1506
                1730
                Original research
                Custom metadata
                unlocked

                Medicine
                statistics & research methods,radiology & imaging,diagnostic radiology,musculoskeletal disorders,knee

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