Accurate characterisation of fractures is essential in fracture management trials. However, this is often hampered by poor inter-observer agreement. This article describes the practicalities of defining the fracture population, based on the Neer classification, within a pragmatic multicentre randomised controlled trial in which surgical treatment was compared with non-surgical treatment in adults with displaced fractures of the proximal humerus involving the surgical neck. The trial manual illustrated the Neer classification of proximal humeral fractures. However, in addition to surgical neck displacement, surgeons assessing patient eligibility reported on whether either or both of the tuberosities were involved. Anonymised electronic versions of baseline radiographs were sought for all 250 trial participants. A protocol, data collection tool and training presentation were developed and tested in a pilot study. These were then used in a formal assessment and classification of the trial fractures by two independent senior orthopaedic shoulder trauma surgeons.Objectives
Methods
Current studies on the additional benefit of using computed tomography
(CT) in order to evaluate the surgeons’ agreement on treatment plans
for fracture are inconsistent. This inconsistency can be explained
by a methodological phenomenon called ‘spectrum bias’, defined as
the bias inherent when investigators choose a population lacking
therapeutic uncertainty for evaluation. The aim of the study is
to determine the influence of spectrum bias on the intra-observer
agreement of treatment plans for fractures of the distal radius. Four surgeons evaluated 51 patients with displaced fractures
of the distal radius at four time points: T1 and T2: conventional
radiographs; T3 and T4: radiographs and additional CT scan (radiograph
and CT). Choice of treatment plan (operative or non-operative) and
therapeutic certainty (five-point scale: very uncertain to very
certain) were rated. To determine the influence of spectrum bias,
the intra-observer agreement was analysed, using Kappa statistics,
for each degree of therapeutic certainty. Objectives
Methods
