Given a passage classify if the passage has a definite objective/aim/goal or not. Output '1' if the passage has a defininte objective/aim/goal and output '0' if the passage does not have a definite objective/aim/goal.

Input: Consider Input: Recent research suggests that preintervention functional magnetic resonance imaging (fMRI) data may predict weight loss outcomes among patients who participate in a behavioral weight loss plan. No study has examined whether presurgical brain activation can predict outcomes following bariatric surgery.', 'The aim of the present study was to determine if brain activations during a presurgical fMRI food-motivation paradigm are associated with weight loss 3 and 6 months following laparoscopic adjustable gastric banding (LAGB). Nineteen participants viewed food and nonfood pictures from a well-established food motivation paradigm during an fMRI scanning session before LAGB surgery. Weight was assessed presurgery and 3 and 6 months postsurgery; data for all participants was available at each time point. fMRI data were analyzed using the BrainVoyager QX statistical package. Whole brain voxelwise correlations of presurgery (food-nonfood) brain activation and weight, corrected for multiple comparisons, were performed to analyze the relationship between presurgical brain activation and subsequent weight loss. The settings were a medical university brain imaging center and 2 surgical weight loss centers in a major metropolitan area.', 'Increased activity in frontal regions associated with cognitive control (medial, middle, superior frontal gyrus) and posterior cingulate cortex was associated with weight loss following LAGB.

Output: 0

Input: Consider Input: Hydrocortisone therapy is based on a dosing regimen derived from estimates of cortisol secretion, but little is known of how the dose should be distributed throughout the 24 h. We have used deconvolution analysis of 24-h serum cortisol profiles to determine 24-h cortisol secretion and distribution to inform hydrocortisone dosing schedules in young children and older adults.', 'Twenty four hour serum cortisol profiles from 80 adults (41 men, aged 60-74 years) and 29 children (24 boys, aged 5-9 years) were subject to deconvolution analysis using an 80-min half-life to ascertain total cortisol secretion and distribution throughout the 24-h period.', 'Mean daily cortisol secretion was similar between adults (6.3 mg/m(2) body surface area/day, range 5.1-9.3) and children (8.0 mg/m(2) body surface area/day, range 5.3-12.0). Peak serum cortisol concentration was higher in children compared with adults, whereas nadir serum cortisol concentrations were similar. Timing of the peak serum cortisol concentration was similar (07.05-07.25), whereas that of the nadir concentration occurred later in adults (midnight) compared with children (22.48) (P = 0.003). Children had the highest percentage of cortisol secretion between 06.00 and 12.00 (38.4%), whereas in adults this took place between midnight and 06.00 (45.2%).

Output: 0

Input: Consider Input: This study aimed to investigate the interaction between gender and other socio-economic characteristics on sleep quality of the patients with Coronary Artery Disease (CAD).', 'This cross sectional study was conducted on 717 patients with CAD. The socio- economic status (education level, income, marital status, and place of residence) was considered as the independent variable. Besides, the study outcome was the quality of sleep which was measured using Pittsburgh Sleep Quality Index (PSQI). Gender was considered as a possible effect modifier. Two-way ANOVA was used to evaluate the interaction between gender and socio-economic factors on sleep quality. As defined by Baron and Kenny, moderator was defined as a variable that affected the direction or magnitude of the association of interest.', 'Female gender, low education level, and low income were predictive of poor sleep quality. Among female (10.0 4.3 vs. 7.6 5.0, P < 0.05), but not male patients (6.7 4.2 vs. 7.0 4.2, P > 0.05), low education was associated with poor sleep quality. Also, among female (10.0 4.3 vs. 5.7 2.5, P < 0.05), but not male patients (7.0 4.2 vs. 6.0 3.8, P > 0.05), low income was predictive of poor sleep quality. Gender did not modify the effect of other socio-economic factors on sleep quality.
Output: 0