Elevated glycohemoglobin HbA1c is associated with low back pain in nonoverweight diabetics

Background Context

Low back pain (LBP) is a common complaint in clinical practice of multifactorial origin. Although obesity has been thought to contribute to LBP primarily by altering the distribution of mechanical loads on the spine, the additional contribution of obesity-related conditions such as diabetes mellitus (DM) to LBP has not been thoroughly examined.

Diagnostic challenges for a novel SH2D1A mutation associated with X‐linked lymphoproliferative disease

Mutations in SH2D1A, encoding the intracellular adaptor signaling lymphocyte activation molecule associated protein (SAP), are associated with X‐linked lymphoproliferative disease type 1 (XLP1). We identified a novel hemizygous SH2D1A c.49G > A (p.E17K) variant in a 21‐year‐old patient with fatal Epstein‐Barr virus infection–associated hemophagocytic lymphohistiocytosis. Cellular and biochemical assays revealed normal expression of the SAP variant protein, yet binding to phosphorylated CD244 receptor was reduced by >95%. Three healthy brothers carried the SH2D1A c.49G > A variant. Thus, data suggest that this variant represents a pathogenic mutation, but with variable expressivity. Importantly, our results highlight challenges in the clinical interpretation of SH2D1A variants and caution in using functional flow cytometry assays for the diagnosis of XLP1.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.     

Minimal invasive pocket technique for magnet bone implant hearing aid without fixation

European Archives of Oto-Rhino-Laryngology - The BAHA (bone-anchored hearing aid) Attract is a magnetic transcutaneous bone conduction device anchored into the temporal bone. The standard surgical...