Talar neck and body fracture outcomes: a multicentre retrospective review.

PURPOSE: Talar neck and body fractures are uncommon injuries that are challenging to manage with high reported complication rates, including post-traumatic arthritis, avascular necrosis, and poor functional outcomes. The aim of this study was to assess the complication rates for patients with talus fractures across three major trauma centres (MTCs) in England. METHODS: A retrospective analysis was performed of prospectively collected trauma databases. Data were collected from three English MTCs. Patients with talar neck and/or body fractures sustained between August 2015 and August 2019 were identified and their clinical course reviewed radiologically and clinically. Isolated process fractures, osteochondral defects and paediatric patients were excluded. Patients were analysed by fracture type and for definitive treatment method with separation into non-operative and operative management groups. Procedure type was identified in the operative group. Superficial infection, deep infection, non-union, avascular necrosis, post-traumatic arthritis and removal of metalwork rates were analysed. RESULTS: Eighty-five patients with talar neck and/or body fractures were included. Seventy-five patients received operative management, 10 non-operative. The overall AVN rate was 5.9% (five patients), overall post-traumatic arthritis rate was 18.8% (16 patients), deep infection rate 1.2% (one patient), non-union rate 4.7% (four patients). Removal of metalwork rate was 9.4% (eight patients). CONCLUSION: Our reported outcomes and complication rates are generally lower than those previously described. This may be a result of improved techniques, a higher frequency of open reduction with direct visualisation or by surgery occurring in centralised specialist centres.

Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.

Importance: An increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost entirely on comparing CDSS directly with clinicians (human vs computer). Little is known about the outcomes of these systems when used as adjuncts to human decision-making (human vs human with computer). Objectives: To conduct a systematic review to investigate the association between the interactive use of ML-based diagnostic CDSSs and clinician performance and to examine the extent of the CDSSs' human factors evaluation. Evidence Review: A search of MEDLINE, Embase, PsycINFO, and grey literature was conducted for the period between January 1, 2010, and May 31, 2019. Peer-reviewed studies published in English comparing human clinician performance with and without interactive use of an ML-based diagnostic CDSSs were included. All metrics used to assess human performance were considered as outcomes. The risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Risk of Bias in Non-Randomised Studies-Intervention (ROBINS-I). Narrative summaries were produced for the main outcomes. Given the heterogeneity of medical conditions, outcomes of interest, and evaluation metrics, no meta-analysis was performed. Findings: A total of 8112 studies were initially retrieved and 5154 abstracts were screened; of these, 37 studies met the inclusion criteria. The median number of participating clinicians was 4 (interquartile range, 3-8). Of the 107 results that reported statistical significance, 54 (50%) were increased by the use of CDSSs, 4 (4%) were decreased, and 49 (46%) showed no change or an unclear change. In the subgroup of studies carried out in representative clinical settings, no association between the use of ML-based diagnostic CDSSs and improved clinician performance could be observed. Interobserver agreement was the commonly reported outcome whose change was the most strongly associated with CDSS use. Four studies (11%) reported on user feedback, and, in all but 1 case, clinicians decided to override at least some of the algorithms' recommendations. Twenty-eight studies (76%) were rated as having a high risk of bias in at least 1 of the 4 QUADAS-2 core domains, and 6 studies (16%) were considered to be at serious or critical risk of bias using ROBINS-I. Conclusions and Relevance: This systematic review found only sparse evidence that the use of ML-based CDSSs is associated with improved clinician diagnostic performance. Most studies had a low number of participants, were at high or unclear risk of bias, and showed little or no consideration for human factors. Caution should be exercised when estimating the current potential of ML to improve human diagnostic performance, and more comprehensive evaluation should be conducted before deploying ML-based CDSSs in clinical settings. The results highlight the importance of considering supported human decisions as end points rather than merely the stand-alone CDSSs outputs.

Novel endoscopic approaches in the diagnosis and management of biliary strictures.

Indeterminate bilary strictures present the clinician with a wide differential diagnosis. Histological confirmation is usually required for treatment, but tissue acquisition remains challenging. Novel developments in endoscopic technology, such as single operator cholangioscopy and confocal endomicroscopy, have led to improvements in diagnostic accuracy in recent years. In patients with non-resectable malignant biliary obstruction, effective biliary decompression improves symptoms and enables patients to undergo palliative therapies. Improvements in endoscopic techniques, biliary stents and the development of local ablative techniques have led to further improvements in stent patency and survival in these patients. In this article, we review emerging diagnostic and therapeutic techniques for the endoscopic management of indeterminate biliary strictures.