Clinical orthopaedics and related research
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Clin. Orthop. Relat. Res. · Oct 2020
Multicenter StudyWhat Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty?
Machine learning techniques can identify complex relationships in large healthcare datasets and build prediction models that better inform physicians in ways that can assist in patient treatment decision-making. In the domain of shoulder arthroplasty, machine learning appears to have the potential to anticipate patients' results after surgery, but this has not been well explored. ⋯ Three different commercially available machine learning techniques were used to train and test models that predicted clinical outcomes after aTSA and rTSA; this device-type comparison was performed to demonstrate how predictive modeling techniques can be used in the near future to help answer unsolved clinical questions and augment decision-making to improve outcomes after shoulder arthroplasty.
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Clin. Orthop. Relat. Res. · Oct 2020
Minimum Clinically Important Differences of the Hospital for Special Surgery Dysphagia and Dysphonia Inventory and Other Dysphagia Measurements in Patients Undergoing ACDF.
Postoperative dysphagia is a common complication after anterior cervical surgery, and it can be measured using patient-reported outcome measures (PROMs). The Hospital for Special Surgery Dysphagia and Dysphonia Inventory (HSS-DDI) is a condition-specific PROM to evaluate dysphagia and dysphonia after anterior cervical discectomy and fusion (ACDF). The minimum clinically important difference (MCID) of the HSS-DDI has not, to our knowledge, been established. Other PROMs have been used to assess dysphagia (SWAL-QOL and MD Anderson Dysphagia Inventory [MDADI]) in ACDF. Currently, few studies have addressed the MCIDs of these PROMs. ⋯ Level III, therapeutic study.
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Clin. Orthop. Relat. Res. · Oct 2020
Delayed Diagnosis Is the Primary Cause of Sarcoma Litigation: Analysis of Malpractice Claims in the United States.
Sarcoma care is highly litigated in medical malpractice claims. Understanding the reasons for litigation and legal outcomes in sarcoma care may help physicians deliver more effective and satisfying care to patients while limiting their legal exposure. However, few studies have described malpractice litigation in sarcoma care. ⋯ Physicians can mitigate their malpractice risk while reducing delays in diagnosis of sarcomas by carefully reviewing all existing diagnostic studies, establishing closed-loop communication protocols to communicate critical findings from diagnostic studies, and developing policies to facilitate second-opinion consultation, particularly for imaging studies, with an experienced sarcoma specialist. Musculoskeletal oncologists may be able to help further reduce the rates of malpractice litigation in sarcoma care by helping patients understand that delays in diagnosis do not necessarily constitute medical malpractice.