Hasil untuk "math.QA"

T. Pommer, L.S. Fog, I.R. Vogelius et al.

R J McNeil, R G Garrett

William Rawlinson

John W. Williamson, Donald E. Moore, Paul J. Sanazaro

In this article we describe an "outcomes framework" for planning and analyzing quality management systems in relation to their ultimate purpose, enhancing the wide range of health care benefits. "Small qa" includes those methods that focus on structure or process and induce improvement of outcomes. These methods are essential but, predictably, often involve minimal improvement of health care. "LARGE QA " is defined as those methods that focus on unacceptable outcomes and deduce processes and structures to be changed to enhance outcomes. These methods focus on either "problems" or "opportunities" that predict substantial improvement in health care benefits. We briefly describe and analyze this outcomes framework for quality management in terms of its conceptual factors and its current and future emphasis. We then describe several major nationalprogram developments and resources that will facilitate moving the balance of quality management effort from small qa to LARGE QA.

Phanindra Sai Boyapati, Sudhakar Allam

Richard Lesieur

Radiotherapy is a common form of cancer treatment with approximately two thirds of patients receiving some form of radiation as part of their care. The increasing complexity of modern radiotherapy treatments has necessitated the practice of patient specific quality assurance (PSQA). PSQA typically is performed prior to a patient’s first treatment and consists of delivering the radiotherapy treatment to a device containing dosimeters. These measurements are compared with calculation using gamma index analysis to ensure the machine is capable of delivering an accurate treatment. However, measurement and calculation never agree perfectly, and it is difficult to estimate how small discrepancies as measured by PSQA devices will translate to the patient’s actual treatment. This has led to interest in log-file based PSQA in which records of the machine state during treatment delivery are used to reconstruct the dose to the patient. This work’s purpose is to compare log-file based PSQA methods to standard measurement based PSQA at a radiotherapy clinic. A group of clinical prostate and head-and-neck treatment plans were anonymized for this work. In specific aim 1, the reproducibility of log-file based PSQA methods is compared to measurement over repeated deliveries on a single machine as well as deliveries on two matched treatment machines. In specific aim 2, the sensitivity of PSQA to simulated treatment delivery errors is compared with measurement by intentionally modifying certain delivery parameters and comparing the results of each PSQA method. In specific aim 3, a variation of log-file based PSQA that compares changes between planned and log-file reconstructed patient dose volume histograms (DVHs) as an alternative to gamma index analysis is tested. The in-house log-file PSQA method performed similarly to the standard measurement based method. All unmodified plans passed both PSQA methods, and variations in the results of x each method across multiple deliveries and multiple machines were also similar. Standard PSQA was more sensitive to simulated errors than log-file PSQA based on gamma index analysis, but log-file PSQA based on DVH metrics was most sensitive, but also resulted in some unmodified plans failing. This work suggests log-file PSQA is a viable alternative to measurement.

Shifaa' Al Qa'qa', Rehab Al-Fatani, Sonia Rodriguez-Ramirez et al.

Hendra Lukito, Harif Amali Rivai

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Gerhard Nahler

L Huiet

The qa-1 regulatory region controls the expression of the three structural genes required for the early reactions in quinic acid catabolism in Neurospora crassa. Genetic analysis previously identified two types of noninducible qa-1 mutants, qa-1S and qa-1F, which mapped in separate non-overlapping regions. These mutations were originally interpreted as defining separate domains of a single regulatory protein. This communication describes the further genetic and physical characterization of the qa-1 regulatory region. Using both Neurospora transformation and DNA . RNA hybridization, it has been shown that the qa-1 region consists of two distinct genes corresponding to the two original mutational types qa-1S and qa-1F. The analysis of the mRNA species hybridizing to these regions indicates that the qa-1F gene encodes a 2.9-kilobase (kb) mRNA, while the qa-1S gene encodes related 4.1-kb and 3.4-kb mRNAs. The transcriptional regulation of one of these genes, qa-1S, was examined. Evidence is presented that the qa-1S gene is induced by quinic acid and is also subject to apparent autogenous regulation as well as to control by the qa-1F gene product. Based on these results and earlier genetic analysis, the hypothesis is proposed that one of the two qa regulatory genes encodes a repressor protein (qa-1S), and the other encodes an activator protein (qa-1F), both of which control qa gene expression.

M.G. CANTALOUB

G.J. Melnyk