In this task, you are given a passage which has a question and the context. You have to generate an answer to the question based on the information present in the context.

Example Input: Context: Despite the potential benefits of total quality management (TQM), many healthcare organisations encountered difficulties in its implementation. The purpose of this paper is to explore the barriers to successful implementation of TQM in healthcare organisations of Iran.', 'This study involved a mixed research design. In-depth interviews were conducted with TQM practitioners to explore TQM implementation obstacles in Iranian healthcare organisations. In addition, this study involved survey-based research on the obstacles associated with successful TQM transformation.', "TQM implementation and its impact depend on the ability of managers to adopt and adapt its values and concepts in professional healthcare organisations. Unsuccessful TQM efforts in Iranian healthcare organisations can be attributed to the non-holistic approach adopted in its implementation, inadequate knowledge of managers' about TQM implementation, frequent top management turnover, poor planning, vague and short-termed improvement goals, lack of consistent managers' and employees' commitment to and involvement in TQM implementation, lack of a corporate quality culture, lack of team orientation, lack of continuous education and training and lack of customer focus. Human resource problems, cultural and strategic problems were the most important obstacles to TQM successful implementation, respectively.\Question: Does why TQM work in Iranian healthcare organisations?
Example Output: Understanding the factors that are likely to obstruct TQM implementation would enable managers to develop more viable strategies for achieving business excellence.

Example Input: Context: The present study aimed to identify the relationship between glycemic variability (GV) and the 10-y risk of cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM) patients with good glycemic control.', 'Two-hundred forty consecutive T2DM patients (HbA1c≤7.0%) without CVD were included to calculate the 10-y CVD risk by Framingham risk score (FRS), and divided into 3 groups: low-risk group (FRS≤10%), intermediate-risk group (>10%, ≤20%) and high-risk group (>20%). Inter-group differences of GV were determined by comparing the SD of blood glucose (SDBG), mean amplitudes of glycemic excursion (MAGE), and mean of daily differences (MODD) gathered from 72-h continuous glucose monitoring system.', 'The levels of SDBG and MAGE significantly increased along with the raises of 10-y CVD risk of T2DM patients (p<0.01). FRS was positively correlated with age, systolic blood pressure, SDBG and MAGE (r=0.717, 0.525, 0.509 and 0.485 respectively, p<0.01), while negatively correlated with the level of HDL-C (r=-0.348, p<0.01). Furthermore, multivariate logistic regression analysis confirmed that increased MAGE [OR: 1.623(1.198-2.316), p<0.001] and patients with high urine albumin excretion rates [OR: 1.743(1.247-2.793), p<0.001] were independent predictors for high 10-y CVD risk.\Question: Is glycemic variability evaluated by continuous glucose monitoring system associated with the 10-y cardiovascular risk of diabetic patients with well-controlled HbA1c?
Example Output: GV predicts independently the 10-y CVD risk of T2DM patients with well-controlled HbA1c.

Example Input: Context: Glutamic acid decarboxylase autoantibodies (GADA) are the most frequent beta-cell-specific autoantibodies in type 1 diabetes and in latent autoimmune diabetes in adults (LADA). The autoimmune attack on pancreatic islet cells is associated with a T helper 1 cell (T(h)1) response, mainly represented by IgG(1)-subclass in humans. It has been proposed that the presence of IgG(4) may be associated with a T(h)2 response. The aim of our study was to compare the GADA IgG-subclass distribution between adult patients with type 1 diabetes and LADA.', 'Patients with type 1 diabetes (n=45) and patients with LADA (n=60) were included. Radioimmunoprecipitation assay with IgG-subclass specific Sepharose (IgG(1), IgG(2), IgG(3) and IgG(4)) was used to precipitate the antibody/antigen-complex.', 'We only detected IgG(4)-subclass of GADA in subjects with LADA (26.7%; p<0.001). IgG(1) was the most common GADA-subclass in both groups, however IgG(1) as the solely expressed subclass was more common among type 1 diabetic patients (77.8%; p<0.05). The rank order of the frequencies of IgG-subclasses in type 1 diabetes was IgG(1)>IgG(3)>IgG(2)>IgG(4) and in LADA patients IgG(1)>IgG(4)>IgG(2)>IgG(3).\Question: Is igG4-subclass of glutamic acid decarboxylase antibody more frequent in latent autoimmune diabetes in adults than in type 1 diabetes?
Example Output:
The difference in GADA IgG-subclasses could indicate a different immune response, possibly an altered balance between T(h)1 and T(h)2 cytokine profile in pancreatic islets. This difference could contribute to the slower rate of beta cell destruction in LADA patients, as reflected by a higher C-peptide level at clinical onset.