Given a passage, construct a question on the basis of the information present in the passage. Construct the question in such a way that (i) it is unambiguous, (ii) it is answerable from the passage, (iii) its answer is unique (iv) it answer uses text phrases from the passage. Avoid creating questions that (i) can't be answered correctly without actually understanding the passage and (ii) uses the same words or phrases given in the passage.

Example Input: The Tonnis radiographic classification of developmental dysplasia of the hip (DDH) has been used as a prognostic indicator for patients with walking-age DDH. The International Hip Dysplasia Institute (IHDI) classification, a new radiographic classification system, has been proposed to be more reliable by its creators. We sought to validate its reliability using independent observers, to compare it to the Tonnis method, and to assess its prognostic significance in a large cohort of patients.', 'A consecutive series of walking-age DDH patients were examined radiographically and classified by the Tonnis and IHDI schemes by 3 independent observers. Interobserver agreement was determined using the Kappa method. Clinical data were collected on patients with regard to success of closed reduction, need for later pelvic osteotomy, and presence of subsequent radiographic avascular necrosis (AVN). The prognostic value of the Tonnis and IHDI classifications to predict these clinical outcomes was determined.', 'A total of 287 hips were available for analysis of the classification schemes. In total, 235 hips underwent attempted closed reduction and were eligible for analysis of successful closed reduction, and 131 hips had >4-year follow-up and were utilized for analysis of late pelvic osteotomy and AVN. Both classifications showed excellent interobserver reliability and in general, there was nonstatistically significant better reliability for the IHDI versus the Tonnis classification. In multivariate analysis, both IHDI and Tonnis classifications were found to be predictive of successful closed reduction and need for late pelvic osteotomy. Both methods showed trends toward being predictive of AVN rate, without statistical significance.
Example Output: Is a New Radiographic Classification System for Developmental Hip Dysplasia Reliable and Predictive of Successful Closed Reduction and Late Pelvic Osteotomy?

Example Input: Overexpression of the human c-MYC (MYC) oncogene is one of the most frequently implicated events in the pathogenesis of hepatocellular carcinoma (HCC). Previously, we have shown in a conditional transgenic mouse model that MYC overexpression is restrained from inducing mitotic cellular division and tumorigenesis in the adult liver; whereas, in marked contrast, MYC induces robust proliferation associated with the very rapid onset of tumorigenesis in embryonic and neonatal mice.', 'Here, we show that non-genotoxic hepatotoxins induce changes in the liver cellular context associated with increased cellular proliferation and enhanced tumorigenesis. Both 5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) and carbon tetrachloride (CCl(4)) cooperate with MYC to greatly accelerate the onset of liver cancer in an adult host to less than 7 days versus a mean latency of onset of over 35 weeks for MYC alone. These hepatotoxin-enhanced liver tumors grossly and histologically resemble embryonic and neonatal liver tumors. Importantly, we found that MYC overexpression is only capable of inducing expression of the mitotic Cyclin B1 in embryonic/neonatal hosts or adult hosts that were treated with either carcinogen.
Example Output: Do hepatotoxin-induced changes in the adult murine liver promote MYC-induced tumorigenesis?

Example Input: To explore the relationship between continuous thrombocytopenia and sepsis in patients with severe burns.', 'Clinical data of 148 severely burned patients admitted to our,two burn centers from January 2007 to December 2011 and conforming to the study criteria were retrospectively analyzed. All patients were divided into sepsis group (n =44) and non-sepsis group (n = 104) according to the presence or absence of sepsis within post burn day (PBD) 30. The data of age, gender, total burn area, full-thickness burn area, fluid infusion volume within post burn hour (PBH) 24, plasma concentration of calcium ion on PBD 1, plasma concentration of albumin on PBD 1, platelet count on PBD 1, acute physiology and chronic health evaluation (APACHE) II score on admission, the presence or absence of hypovolemic shock or inhalation injury on admission, the presence or absence of disseminated intravascular coagulation (DIC) within PBH 48, operation or no operation within PBD 3, thrombocytopenia duration within PBD 10, and mortality were statistically compared between two groups to screen the independent risk factors of sepsis. Data were processed with t test, chi-square test, single factor Logistic regression analysis, and multi-factor Logistic regression analysis.', 'Between two groups, there were statistically significant differences in total burn area, full-thickness burn area, plasma concentration of calcium ion on PBD 1, plasma concentration of albumin on PBD 1, APACHE II score on admission, presence or absence of hypovolem- ic shock on admission, presence or absence of inhalation injury on admission, presence or absence of DIC within PBH 48, and mortality (with t values from 2.433 to 4.082, χ2 values from 8. 818 to 31.528, P < 0.05 or P < 0.01). Furthermore, the duration of thrombocytopenia within PBD 10 in sepsis group was (5.2 ± 2.4) d, which was significantly longer than that in non-sepsis group [(2.9 ± 1.9) d, t =6. 189, P <0.01]. There were no statistically significant differences in the other indexes between two groups (with t values from 0.971 to 1. 250, χ2 values respectively 0. 054 and 1.529, P values above 0.05). Single factor and multi-factor Logistic regression analysis indicated that APACHE II score on admission and duration of thrombocytopenia within PBD 10 were closely related to occurrence of sepsis (with odds ratio respectively 1. 140 and 1.569, P values below 0.01).
Example Output:
Do [ Clinical significance of continuous thrombocytopenia in predicting sepsis after severe burn ]?