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.

Example Input: To derive a nomogram of fetal maxillary bone length (MAX) for a Chinese population and to study whether first-trimester maternal serum levels of pregnancy-associated plasma protein-A (PAPP-A) is an independent predictor of fetal MAX.', 'This was a prospective observational study over 10 months examining Chinese women with a singleton pregnancy who attended the first-trimester screening program for Down syndrome. The subjects had the fetal crown-rump length (CRL), nuchal translucency, MAX, maternal serum PAPP-A and free beta-human chorionic gonadotropin (fbeta-hCG) levels measured. A nomogram of MAX was derived using normal pregnancies with reliable dates. The correlations between MAX, CRL, PAPP-A and fbeta-hCG levels were studied, after correction for gestational dependency using Z-score transformation for the ultrasound markers (Z-MAX and Z-CRL), and logarithmic transformation of multiple of gestation-specific medians for the biochemical markers (log10PAPP-A multiples of the median (MoM) and log10fbeta-hCG MoM), using the Pearson test and multiple regression analysis.', 'During the study period, 607 Chinese women met the inclusion criteria. The mean gestational age at the first-trimester screening was 12 + 4 weeks (SD, 4 days). Curve estimation analysis showed that a linear relationship fit best between MAX and CRL (MAX (mm) = -0.01 + 0.101 * CRL (mm); r = 0.826; P < 0.0001), and between MAX and gestational age (MAX (mm) = -8.465 + 0.170 * gestational age (day); r = 0.754; SD = 0.71; P < 0.0001). There were significant positive correlations between Z-MAX and Z-CRL (r = 0.627; P = < 0.0001), Z-MAX and log10PAPP-A MoM (r = 0.239; P = < 0.0001). Multiple regression analysis showed that both Z-CRL (P = < 0.0001) and log10 PAPP-A MoM (P = 0.048) were independent predictors for Z-MAX.
Example Output: 1

Example Input: To determine the association of ethanol intoxication with hypoglycemia in ED patients.', 'Retrospective, laboratory log review of 953 consecutive patients who were evaluated for ethanol intoxication in an urban university hospital ED over a three-month period. Simultaneous serum glucose determination was carried out for each patient and associations between ethanol level and glucose were sought.', 'Glucose concentrations were unavailable for 16 patients (1.7%). Of the remaining patients, 584 patients had detectable ethanol concentrations (ethanol-positive), and 353 had no detectable ethanol (ethanol-negative). Ethanol concentrations (mean +/- SD) in the ethanol-positive group were 50.11 +/- 24.08 mmol/L (231 +/- 111 mg/dL), and glucose concentrations were 5.83 +/- 1.94 mmol/L (105 +/- 35 mg/dL). Hypoglycemia [glucose < 3.72 mmol/L (67 mg/dL)] was observed for five (0.9%) ethanol-positive patients. It was classified as mild-moderate [2.78-3.66 mmol/L (50-66 mg/dL)] for four patients (0.7%) and severe [< 2.78 mmol/L (50 mg/dL)] for one (0.2%). Ethanol concentrations ranged from 25.60 to 68.33 mmol/L (118 to 315 mg/dL). There was no correlation between ethanol and glucose concentrations in any subset of the ethanol-positive patients population. In the ethanol-positive group, patients who had several ethanol-positive visits (56 patients, mean 3.6 visits/patient) accounted for four of the five episodes of hypoglycemia, including the one episode of severe hypoglycemia. The frequency of hypoglycemia in repeat visitors (2.0%) was higher than that in the group of patients without repeat visits (0.2%). In the ethanol-negative group, there were four (1.1%) episodes of mild-moderate hypoglycemia and no severely hypoglycemic patients. Hypoglycemia was not more likely to occur among ethanol-positive patients than it was among ethanol-negative patients.
Example Output: 1

Example Input: Medical knowledge is essential for appropriate patient care; however, the accuracy of internal medicine (IM) residents' assessment of their medical knowledge is unknown.", 'IM residents predicted their overall percentile performance 1 week (on average) before and after taking the in-training exam (ITE), an objective and well accepted method to assess medical knowledge to study resident assessment accuracy. Ordinary least squares regression was used to study the association between the absolute accuracy of their predictions of their percentile performance on the ITE examination and their actual percentile performance.', 'Ninety-three percent of our 28 residents participated. Residents were highly inaccurate in predicting their percentile performance. Only 31% had ITE scores that were within 10 points of their predictions. On average, most residents were pessimistic about their overall percentile performance with 18 (69%) underestimating their performance. Having just taken the examination and previous experience with the examination did not improve predictions of percentile performance.
Example Output:
0