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: Interobserver and intraobserver reliability study of improved method to evaluate radiographs of patients with scoliosis.', 'To determine the reliability of a computer-assisted measurement protocol for evaluating Cobb angle and King et al classification.', 'Evaluation of scoliosis radiographs is inherently unreliable because of technical and human judgmental errors. Objective, computer-assisted evaluation tools may improve reliability.', 'Posteroanterior preoperative radiographic images of 27 patients with adolescent idiopathic scoliosis were each displayed on a computer screen. They were marked 3 times in random sequence by each of 5 evaluators (observers) who marked 70 standardized points on the vertebrae and sacrum in each radiograph. A computer program (Spine 2002;27:2801-5) that identified curves, calculated Cobb angles, and generated the King et al classification automatically analyzed coordinates of these points. The interobserver and intraobserver variability of the Cobb angle and King et al classification evaluations were quantified and compared with values obtained by unassisted observers.', 'Average Cobb angle intraobserver standard deviation was 2.0 degrees for both the thoracic and lumbar curves (range 0.1 to 8.3 degrees for different curves). Interobserver reliability was 2.5 degrees for thoracic curves and 2.6 degrees for lumbar curves. Among the 5 observers, there was an inverse relationship between repeatability and time spent marking images, and no correlation with image quality or curve magnitude. Kappa values for the variability of the King et al classification averaged 0.85 (intraobserver).\Question: Do computer-assisted algorithms improve reliability of King classification and Cobb angle measurement of scoliosis?
Example Output: Variability of Cobb measurements compares favorably with previously published series. The classification was more reliable than achieved by unassisted observers evaluating the same radiographs. The same principles may be applicable to other radiographic measurement and evaluation procedures.

Example Input: Context: The measurement of affective symptoms in older persons who decline cognitively is uncertain. The authors investigated whether mood variability predicts dementia in patients with age-related macular degeneration (AMD).', 'Three-year observational study after a clinical trial.', 'Community follow-up of outpatients ascertained from retina clinics.', 'One hundred sixty patients with AMD.', "Geriatric Depression Scale (GDS) administered every 2 weeks for 6 months to subjects; Informant Questionnaire for Cognitive Decline in the Elderly (IQCODE) administered to subjects' knowledgeable informants.", 'Twenty-three subjects (14.4%) declined cognitively. Age, education, baseline GDS score > or =5, and variability in GDS scores (i.e., fluctuations between adjacent time points) were associated with cognitive decline. For GDS variability, each 1 unit increase in the residual standard deviation (SD) of the GDS increased the risk for cognitive decline by 93% (IDR = 1.92; 95% CI [1.27-2.91]). Thus, subjects with a residual SD of 1 were nearly twice as likely to become demented as subjects with no variability in GDS scores. The risk for subjects with SDs of 2 increased more than threefold (IDR = 3.68; 95% CI [1.61-8.47]). A multiple regression analysis showed that GDS variability was a significant risk factor for dementia after controlling for significant covariates.\Question: Does variability in depressive symptoms predict cognitive decline in age-related macular degeneration?
Example Output: These data suggest a useful approach to conceptualizing and measuring depressive symptoms in older persons. Variability in self-reported mood may be an early sign of dementia and may offer new insights into the neurobiological mechanisms linking depression and cognition

Example Input: Context: Several biological markers have been related to prognosis in mammary ductal carcinoma. The aim of the study was to determine biological markers that could predict local recurrence following treatment for all stages of primary operable ductal carcinoma of the breast.', 'A consecutive series of patients treated for pure ductal carcinoma in situ (DCIS, n = 110) and invasive ductal carcinoma (IDC, n = 243) was studied. Twenty-three patients with DCIS were excluded because of lack of original paraffin embedded tissue. All patients had been treated between July 1996 and December 2001. Median follow-up was 49.8 mo. From the original paraffin embedded tumors, tissue microarrays (TMAs) were constructed. On these TMAs, immunohistochemistry was performed for estrogen-receptor (ER), progesterone-receptor (PR), Her2/neu, p53, and cyclin D1. Main outcome was the event of LR. All analyses were stratified for diagnosis (DCIS or IDC) and pathological grade.', 'In univariate analyses, Her2/neu overexpression (hazard ratio [HR] 3.1, 95% confidence interval [CI] 1.1-8.7, P = 0.032) and p53 overexpression (HR 3.5, 95% CI 1.3-9.3, P = 0.014) were associated with LR in patients treated for both DCIS and IDC. In multivariate analysis, p53 overexpression (HR 3.0, 95% CI 1.1-8.2, P = 0.036 and HR 4.4, 95% CI 1.5-12.9, P = 0.008) and adjuvant radiotherapy (HR 0.2, 95% CI 0.1-0.8, P = 0.026) were independent common predictors of LR in patients who had received treatment for both DCIS and IDC.\Question: Is p53 overexpression a predictor of local recurrence after treatment for both in situ and invasive ductal carcinoma of the breast?
Example Output:
p53 overexpression is a common predictor of LR following treatment for all stages of primary operable ductal carcinoma of the breast. This marker may help in planning optimal treatment and follow-up.