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steoarthritis affects more than 500 million people around the world, and it is a leading cause of disability. 1 Non-pharmacological strategies, such as exercise and maintaining a healthy weight, are recommended for first line management, as are simple analgesics, such as nonsteroidal anti-inflammatory drugs (NSAIDs) and paracetamol (acetaminophen). 2 However, advice on using opioid analgesics to treat the pain of knee and hip osteoarthritis is inconsistent; 3,4 opioids are often prescribed, including for about 40% of people with knee osteoarthritis in the United States. 5 Systematic reviews of placebo-controlled trials of the effectiveness of opioids for treating osteoarthritis pain have been limited in scope. For example, one evaluated only opioid treatments of at least four weeks' duration, 6 while a second was restricted to oral opioid therapy. 7 GRADE ratings were not always reported in the abstract or conclusions, and the validity of opioid dose-response analyses were sometimes unclear. 8 One review excluded tramadol because it had been examined in a separate review. 9 We therefore undertook a systematic review to provide a comprehensive evaluation of the efficacy and safety of opioid analgesic therapy regimens for people with osteoarthritis, and to explore dose-effect relationships. MethodsWe searched MEDLINE, EMBASE, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, the Allied and Complementary Medicine Database (AMED), the Cochrane Database of Systematic Reviews, and the Cochrane Central Register of Controlled Trials (CENTRAL) for eligible randomised controlled trials (RCTs) published in any language in peer-reviewed journals to 31 October 2020 (Supporting Information, table 1). To avoid overestimating treatment effects, 10 we also searched the World Health Organization International Clinical Trials Registry (https://trial search.who.int) for unpublished trials. We screened the reference lists of retrieved publications to identify further RCTs. Our systematic review was prospectively registered with PROSPERO (CRD420191142813; 16 October 2019). Inclusion criteriaWe included RCTs in which the analgesic effect of an opioid was compared with that of placebo in people with osteoarthritis of any type (knee, hip, hand, spine) of any duration. All single ingredient and combination opioid-containing analgesic regimens were included, regardless of opioid dose and administration route. We did not include trials in which the effect of opioid therapy was compared with other treatments but not with placebo. Trials including a range of pain conditions were eligible if data for participants with osteoarthritis could be extracted. Study selectionThree authors (CAS, WA, GZ) independently screened the titles and abstracts and read the full text of potentially eligible publications; disagreements were resolved by consensus.

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Objectives: To determine the incidence of hypovitaminosis D in proximal femur fracture (PFF) patients and investigate whether sociodemographic factors or radiographic parameters are associated with vitamin D levels. Methods: This is a consecutive case series of SouthEast Queensland patients presenting with low-energy PFFs. Vitamin D levels and sociodemographic factors (age, sex, postcode, medications and type of residence) were collected from medical records. Radiographic parameters included PFF type and cortical thickness of the femur. Results: A total of 313 patients were included (mean age = 79.5 years), and 105 (34%) were deficient in vitamin D (<50 nmol/L). There was no association between vitamin D levels and sociodemographic factors or radiographic parameters. Eightyfour (84%) of vitamin D-deficient patients were not taking vitamin D supplements. Conclusions: Social and demographic factors are not correlated with vitamin D levels in this cohort. Routine vitamin D supplementation may be indicated in ageing patients although it is not always protective of low-energy fractures.

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Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related deaths globally. Unlike most other cancers, HCC can be diagnosed solely on imaging for high-risk patients. However, this is frequently complicated by atypical or indeterminate features necessitating biopsy or close follow-up with serial imaging. Artificial intelligence (AI) has the potential to allow for more accurate tumour classification and, thus, avoid unnecessary biopsies. Additionally, earlier diagnosis opens up the potential for curative therapies and improves patient outcomes. A number of artificial intelligence models, including machine learning, convolutional neural networks and radiomics-based models have been tested on ultrasound, CT and MRI images of liver lesions. The following review will outline the most impactful papers in this field.

BackgroundFollowing wrist fracture, it is desirable to identify patients with osteoporosis. A smartphone‐based application (BoneGauge) that uses second metacarpal cortical thickness ratio (2MCP) measured on X‐ray has been proposed. This study aims to validate this application using dual‐energy X‐ray absorptiometry scan in a cohort of patients with distal radius fractures.MethodsThirty subjects aged 50 and over who sustained low‐energy fractures of the radius were recruited and measurements were completed by two independent observers using the application.ResultsThe interrater reliability as a screening tool was insufficient (κ = 0.61). Using the 2MCP threshold of 60% for detection of osteopaenia or osteoporosis, we found insufficient correlation between the dual‐energy X‐ray absorptiometry scan and the two sets of readings using the application (κ = 0.28 and 0.35, respectively).ConclusionOn the basis of these results, 2MCP of 60% is not sensitive enough to be used as a screening tool via a smartphone application for assessment of osteoporosis risk.