Neurosurgery
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Surgeons rely on clinical experience when making predictions about treatment effects. Incorporating algorithm-based predictions of symptom improvement after carpal tunnel release (CTR) could support medical decision-making. However, these algorithm-based predictions need to outperform predictions made by surgeons to add value. We compared predictions of a validated prediction model for symptom improvement after CTR with predictions made by surgeons. ⋯ The prediction model outperformed surgeon predictions of improvement after CTR in terms of calibration, accuracy, and sensitivity. Furthermore, the net benefit analysis indicated that using the prediction model instead of relying solely on surgeon decision-making increases the number of patients who will improve after CTR, without increasing the number of unnecessary surgeries.
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Greater thecal sac volumes are associated with an increased risk of spinal anesthesia (SA) failure. The thecal sac cross-sectional area accurately predicts thecal sac volume. The thecal sac area may be used to adjust the dose and prevent anesthetic failure. We aim to assess the rate of SA failure in a prospective cohort of lumbar surgery patients who receive an individualized dose of bupivacaine based on preoperative measurement of their thecal sac area. ⋯ Adjusting the dose of SA according to thecal sac area significantly reduces the rate of SA failure in patients undergoing lumbar spine surgery.