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Error traps in the perioperative management of children with type 1 diabetes
Pediatric Anesthesia, 2023AbstractPatients with type 1 diabetes mellitus (T1D) require insulin administration at all times to maintain euglycemia and metabolic stability. Insulin administration in the perioperative period is complicated by fasting requirements and perioperative stressors that can change the patient's insulin needs. In addition, many anesthesia providers are not
M. Hoagland +4 more
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Refractive Error and Retinopathy Outcomes in Type 1 Diabetes
Ophthalmology, 2021To determine the relationship between refractive error and diabetic retinopathy (DR).Clinical trial.Type I diabetes individuals with serial refractive error and DR stage measurements over 30 years in the Diabetes Control and Complications Trial (DCCT) and Epidemiology of Diabetes Interventions and Complications (EDIC) follow-up study.Stage of DR was ...
Dean P. Hainsworth +8 more
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Biometric and refractive errors evaluation in patients with neurofibromatosis type 1
European Journal of Ophthalmology, 2020Purpose: To analyze biometric changes and prevalence of refractive in patients with neurofibromatosis type 1 (NF1). Methods: Retrospective, case-controlled study involving patients affected by NF1 and healthy ...
Aldo Vagge +7 more
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Dynamic Errors in Type 1 Contouring Systems
IEEE Transactions on Industry Applications, 1972The general nature of contouring errors experienced in type 1 servo systems as a result of command contours is described. Both transient and steady-state errors are discussed. A significant result illustrates that contouring error is essentially a result of mismatched gains in the servo drives and the contour.
Aun-Neow Poo +2 more
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Evaluation of Meal Carbohydrate Counting Errors in Patients with Type 1 Diabetes
Experimental and Clinical Endocrinology & Diabetes, 2021Abstract Aim Correct estimation of meal carbohydrate content is a prerequisite for successful intensified insulin therapy in patients with diabetes. In this survey, the counting error in adult patients with type 1 diabetes was investigated.
Sina Buck +8 more
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Controlling the type 1 error rate in non‐inferiority trials
Statistics in Medicine, 2007AbstractTwo different approaches have been proposed for establishing the efficacy of an experimental therapy through a non‐inferiority trial: The fixed‐margin approach involves first defining a non‐inferiority margin and then demonstrating that the experimental therapy is not worse than the control by more than this amount, and the synthesis approach ...
Steven, Snapinn, Qi, Jiang
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Statistical Guideline #7 Adjust Type 1 Error in Multiple Testing
International Journal of Behavioral Medicine, 2022This is one in a series of statistical guidelines designed to highlight common statistical considerations in behavioral medicine research. The goal is to briefly discuss appropriate ways to analyze and present data in the International Journal of Behavioral Medicine (IJBM).
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Uncertainties and Modeling Errors of Type 1 Diabetes Models
2015Modeling and control are tightly connected if we want to guarantee safety and reliability. These are minimum requirements in the medical field. The more sophisticated methods usually require information beyond the available measurements, and one way or another incorporate all a priori knowledge.
Levente Kovács, Péter Szalay
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Controlling type 1 error rate for sequential, bioequivalence studies with crossover designs
Pharmaceutical Statistics, 2018SummarySample size reestimation in a crossover, bioequivalence study can be a useful adaptive design tool, particularly when the intrasubject variability of the drug formulation under investigation is not well understood. When sample size reestimation is done based on an interim estimate of the intrasubject variability and bioequivalence is tested ...
Hans E. Rasmussen +2 more
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Impact of missing data on type 1 error rates in non‐inferiority trials
Pharmaceutical Statistics, 2010AbstractIn this paper, a simulation study is conducted to systematically investigate the impact of different types of missing data on six different statistical analyses: four different likelihood‐based linear mixed effects models and analysis of covariance (ANCOVA) using two different data sets, in non‐inferiority trial settings for the analysis of ...
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