Kim, Grace

AbstractThis meta‐analysis investigated the effects of exercise on Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) scores following vertebroplasty or kyphoplasty in osteoporotic fractures. A literature search of PubMed, EMBASE (Elsevier), CiNAHL, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Scopus, and Web of Science was conducted from database inception to October 6, 2022. Eligible studies reported osteoporosis patients over 18 years of age with a diagnosis of at least one vertebral fracture via radiography or clinical assessment. This review was registered in PROSPERO (ID: CRD42022340791). Ten studies met the eligibility criteria (n = 889). VAS scores at baseline were 7.75 (95% CI: 7.54, 7.97, I2 = 76.11%). Following initiation of exercise, VAS scores at the endpoint of 12 months were 1.91 (95% CI: 1.53, 2.29, I2 = 92.69%). ODI scores at baseline were 68.66 (95% CI: 56.19, 81.13, I2 = 85%). Following initiation of exercise, ODI scores at the endpoint of 12 months were 21.20 (95% CI: 14.52, 27.87, I2 = 99.30). A two‐arm analysis demonstrated improved VAS and ODI for the exercise group compared to non‐exercise control at 6 months (MD = −0.70, 95% CI: −1.08, −0.32, I2 = 87% and MD = −6.48, 95% CI: −7.52, −5.44, I2 = 46%, respectively) and 12 months (MD = −0.88, 95% CI: −1.27, −0.49, I2 = 85% and MD = −9.62, 95% CI: −13.24, −5.99, I2 = 93%). Refracture was the only adverse event reported and occurred almost twice as frequently in the non‐exercise group than in the exercise group. Exercise rehabilitation post vertebral augmentation is associated with improved pain and functionality, particularly after 6 months of exposure, and may reduce refracture rate.

Abstract Background Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality – for the specific purpose of assessing AI/ML improvements – is currently missing. Methods A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on “ AI in the Laboratory of the Future” prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. Results The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. Conclusions A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.

Currently, there are no internationally accepted consensus guidelines for pathologic evaluation of posttherapy pancreatectomy specimens. The Neoadjuvant Therapy Working Group of Pancreatobiliary Pathology Society was formed in 2018 to review grossing protocols, literature, and major issues and to develop recommendations for pathologic evaluation of posttherapy pancreatectomy specimens. The working group generated the following recommendations: (1) Systematic and standardized grossing and sampling protocols should be adopted for pancreatectomy specimens for treated pancreatic ductal adenocarcinoma (PDAC). (2) Consecutive mapping sections along the largest gross tumor dimension are recommended to validate tumor size by histology as required by the College of American Pathologists (CAP) cancer protocol. (3) Tumor size of treated PDACs should be measured microscopically as the largest dimension of tumor outer limits that is bound by viable tumor cells, including intervening stroma. (4) The MD Anderson grading system for tumor response has a better correlation with prognosis and better interobserver concordance among pathologists than does the CAP system. (5) A case should not be classified as a complete response unless the entire pancreas, peripancreatic tissues, ampulla of Vater, common bile duct, and duodenum adjacent to the pancreas are submitted for microscopic examination. (6) Future studies on tumor response of lymph node metastases, molecular and/or immunohistochemical markers, as well as application of artificial intelligence in grading tumor response of treated PDAC are needed. In summary, systematic, standardized pathologic evaluation, accurate tumor size measurement, and reproducible tumor response grading to neoadjuvant therapy are needed for optimal patient care. The criteria and discussions provided here may provide guidance towards these goals.