A validated ML tool estimates 1-year mortality risk at first presentation in elderly proximal humerus fracture patients, using data already available at the time of the injury. The output is for the consent conversation, not the operative decision.
Before you read anything about the algorithm in this paper, consider the number it was built around: 31.1%. That is the proportion of elderly patients who were completely opioid-naïve before their hip fracture and were still collecting opioid prescriptions a year later. Nearly one in three. In a cohort of more than 26,000 patients drawn … Read more
Evidence-reviewed. Citations throughout. A paper lands in your inbox. A machine learning model for distinguishing spinal tuberculosis from pyogenic vertebral infection and spinal metastasis on MRI. Accuracy: 98.3%. The abstract is confident. The supplementary figures are polished. Your procurement team is interested. Before you endorse it, do some arithmetic. This paper (PMID 42082966) is an … Read more
Evidence-reviewed. Citations throughout. A patient presents to the emergency department with a spreading soft tissue infection and fever. The orthopaedic team is asked to review. Inflammatory markers are elevated, the limb is swollen and tender, and the clinical picture is consistent with serious deep infection — but the critical question is not yet answered: is … Read more
Evidence-reviewed. Citations throughout. A paper lands in your inbox. The headline: a deep learning algorithm achieves 94% accuracy in fracture detection, outperforming both radiologists and orthopaedic surgeons. A colleague forwards it with a one-line message: “thoughts?” Whether your reaction is excitement, scepticism, or indifference, the answer depends on understanding what kind of system this actually … Read more