Three radiological methods are commonly used to assess the outcome of total hip replacement (THR). They aim to record the appearance of lucent areas and migration of the prosthesis in a reproducible manner. Two of them were designed to monitor the implant through time and one to grade the quality of cementing. We have measured the level of inter-and intraobserver agreement in all three systems. We randomised 30 patients to receive either finger packing or retrograde gun cementing during Charnley hip replacements. The postoperative departmental radiographs were evaluated in a blinded study by two orthopaedic trainees, two consultants and two experts in THR. The trainees and consultants repeated the exercise at least two weeks later. We used the unweighted kappa statistic to establish the levels of agreement. In general, intraobserver agreement was moderate but interobserver agreement was poor, with levels similar to or less than those expected by chance. Our results indicate that such systems cannot provide reliable data from centres in different parts of the world, with various levels of surgeon evaluating radiographs at differing time intervals. We discuss the problem and suggest some methods of improvement.

The assessment of surgical skills is an essential part of medical training. The prevalent manual evaluations by expert surgeons are time consuming and often their outcomes vary substantially from one observer to another. We present a videobased framework for automated evaluation of surgical skills based on the Objective Structured Assessment of Technical Skills (OSATS) criteria. We encode the motion dynamics via frame kernel matrices, and represent the motion granularity by texture features. Linear discriminant analysis is used to derive a reduced dimensionality feature space followed by linear regression to predict OSATS skill scores. We achieve statistically significant correlation (p-value <0.01) between the ground-truth (given by domain experts) and the OSATS scores predicted by our framework.

Osteoarthritis is a serious joint disease that causes pain and functional disability for a quarter of a billion people worldwide 1 , with no disease-stratifying tools nor modifying therapy. Here, we use primary chondrocytes, synoviocytes and peripheral blood from patients with osteoarthritis to construct a molecular quantitative trait locus map of gene expression and protein abundance in disease. By integrating data across omics levels, we identify likely effector genes for osteoarthritis-associated genetic signals. We detect stark molecular differences between macroscopically intact (low-grade) and highly degenerated (high-grade) cartilage, reflecting activation of the extracellular matrix-receptor interaction pathway. Using unsupervised consensus clustering on transcriptome-wide sequencing, we identify molecularly-defined patient subgroups that correlate with clinical characteristics. Between-cluster differences are driven by inflammation, presenting the opportunity to stratify patients on the basis of their molecular profile for tailored intervention. We construct and validate a 7-gene classifier that reproducibly distinguishes between these disease subtypes. Finally, we identify potentially actionable compounds for disease modification and drug repositioning. Our findings contribute to both patient stratification and therapy development in this globally important area of unmet need.

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