A/B testing, also known as bucket testing, split testing, or controlled experiment, is a standard way to evaluate user engagement or satisfaction from a new service, feature, or product. It is widely used among online websites, including social network sites such as Facebook, LinkedIn, and Twitter to make data-driven decisions. At LinkedIn, we have seen tremendous growth of controlled experiments over time, with now over 400 concurrent experiments running per day. General A/B testing frameworks and methodologies, including challenges and pitfalls, have been discussed extensively in several previous KDD work [7,8,9,10]. In this paper, we describe in depth the experimentation platform we have built at LinkedIn and the challenges that arise particularly when running A/B tests at large scale in a social network setting. We start with an introduction of the experimentation platform and how it is built to handle each step of the A/B testing process at LinkedIn, from designing and deploying experiments to analyzing them. It is then followed by discussions on several more sophisticated A/B testing scenarios, such as running offline experiments and addressing the network effect, where one user's action can influence that of another. Lastly, we talk about features and processes that are crucial for building a strong experimentation culture.
INTRODUCTION: Proteinuria is a hallmark finding in preeclampsia and a value of 300 mg or more in a 24 hour urine collection aids the diagnosis. A random urine protein-creatinine ratio (UPCR) of at least 0.3 mg/dL has emerged as a surrogate indicator of proteinuria. Point-of-care (POC) UPCR tests have been developed and may provide a convenient and timely extrapolation of proteinuria at the bedside. Although such testing has demonstrated excellent correlation with central laboratory methods in studies of non-pregnant patients, its validity and clinical utility have yet to be established in the pregnant population. Our objective was to evaluate POC UPCR as an indicator of proteinuria in pregnancy and compare it to the central laboratory method. METHODS: 108 urine samples from pregnant women presenting to triage for preeclampsia evaluation were collected prospectively from January to June 2015. For clinical purposes, a urine specimen was collected and sent to the laboratory for standard measurement of protein, creatinine and PC ratio using a Beckman Coulter Au5800, measuring turbidimetry by immunocomplexation with an anti-albumin antibody. A sample of the collected urine was tested via POC UPCR using Multistix Pro 11 reagent strips. Utilizing the manufacture interpretation guide, a “normal” strip ratio was defined as urine containing less than 300 mg protein/g creatinine. RESULTS: There was moderate agreement between POC UPCR and laboratory UPCR (k=0.512). After triage evaluation was complete, 31 women had a clinical diagnosis of preeclampsia. CONCLUSION: Results indicate that the POC UPCR is not reliable for the diagnosis of proteinuria when compared to the central laboratory UPCR.
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