Orthobiologic therapies show significant promise to improve outcomes for patients with musculoskeletal pathology. There are considerable research efforts to develop strategies that seek to modulate the biological environment to promote tissue regeneration and healing and/or provide symptomatic relief. However, the regulatory pathways overseeing the clinical translation of these therapies are complex, with considerable worldwide variation.
View Article and Find Full Text PDFInterest and research in biologic approaches for tissue healing are exponentially growing for a variety of musculoskeletal conditions. The recent hype concerning musculoskeletal biological therapies (including viscosupplementation, platelet-rich plasma, and cellular therapies, or "stem cells") is driven by several factors, including demand by patients promising regenerative evidence supported by substantial basic and translational work, as well as commercial endeavors that complicate the scientific and lay understanding of biological therapy outcomes. While significant improvements have been made in the field, further basic and preclinical research and well-designed randomized clinical trials are needed to better elucidate the optimal indications, processing techniques, delivery, and outcome assessment.
View Article and Find Full Text PDFRationale And Objectives: To evaluate variability in the clinical assessment of breast images, we evaluated scoring behavior of radiologists in a retrospective reader study combining x-ray mammography (XRM) and three-dimensional automated breast ultrasound (ABUS) for breast cancer detection in women with dense breasts.
Methods: The study involved 17 breast radiologists in a sequential study design with readers first interpreting XRM-alone followed by an interpretation of combined XRM + ABUS. Each interpretation included a forced Breast Imaging Reporting and Data System scale and a likelihood that the woman had breast cancer.
Purpose: The authors developed scaling methods that monotonically transform the output of one classifier to the "scale" of another. Such transformations affect the distribution of classifier output while leaving the ROC curve unchanged. In particular, they investigated transformations between radiologists and computer classifiers, with the goal of addressing the problem of comparing and interpreting case-specific values of output from two classifiers.
View Article and Find Full Text PDFRationale And Objectives: Semiparametric methods provide smooth and continuous receiver operating characteristic (ROC) curve fits to ordinal test results and require only that the data follow some unknown monotonic transformation of the model's assumed distributions. The quantitative relationship between cutoff settings or individual test-result values on the data scale and points on the estimated ROC curve is lost in this procedure, however. To recover that relationship in a principled way, we propose a new algorithm for "proper" ROC curves and illustrate it by use of the proper binormal model.
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