ライフサイエンスコーパス: variables

1 Frailty, as calculated by the 11 variables on the mFI.

2 orithm improves class discrimination and indicates that 113 variables including size, electronegativity, number of valenc

3 An mPICH score was developed after variables associated with poor outcome were identified at mul

4 an in normal-birth-weight children after adjustment for all variables.

5 In meta-regression analysis, variables significantly associated with conversion surgery we

6 epwise Cox regression and the Kaplan-Meier method to assess variables obtained at baseline and at first follow-up RHC.

7 More interestingly, a change of coupling between both variables is observed during deep hypothermic CPB in all subj

8 d also may be applied in the investigation of other climate variables.

9 The scoring system consisted of clinical variables (male sex and previous percutaneous coronary interv

10 Demographic, laboratory, and comorbidity variables measured prior to discharge.

11 The effect was independent of potential confounding variables, including maternal socioeconomic status, obstetric

12 [LDL-C], total cholesterol [TC]) were studied as continuous variables, and results are reported per SD increase of each l

13 l analysis included a 2-sample t test to compare continuous variables, chi-square testing for categorical comparisons, an

14 regression controlling for injury severity and demographic variables, the difference in LOS for Medicaid vs non-Medicaid

15 Data elements were extracted and narratively described variables synthesised into four categories.

16 suggest that the variation and complexity of climate-driven variables could be important for understanding the potential

17 The EEG variables were interpreted using standardized terminology by

18 timespans analyzed, even when controlling for environmental variables.

19 ven adequate ethical approval and availability of essential variables.

20 ogy (EFO) is an application ontology driven by experimental variables including cell lines to organize and describe the d

21 ell lines to organize and describe the diverse experimental variables and data resided in the EMBL-EBI resources.

22 A number of experimental variables (e.g., a composition and pH of the supporting elect

23 m" must be examined carefully along with other experimental variables, keeping in mind that the effects of EVs may be acc

24 We identified the following variables as unfavourable predictors of survival.

25 Models constructed from variables present in sensory references performed similarly t

26 Similarly, TandemHeart improves hemodynamic variables but not survival.

27 andomization is the use of genetic variants as instrumental variables to estimate causal effects of risk factors on outco

28 A quasi-experimental instrumental variables probit model of the association correlation of ECT

29 This work regulates several key variables of PbS NC assembly (e.g., NC concentration and solu

30 ely age, sex, and ethnicity; however, several patient-level variables could not be included in our analysis because of hi

31 ute respiratory distress syndrome onset, neither mechanical variables nor PaO2/FIO2 were associated with mortality.

32 Metabolic variables, including glucose, insulin, insulin-like growth fa

33 re chemically analysed and employed to reduce the number of variables used to construct the models.

34 known to reflect a range of psychological and physiological variables, including cognitive effort, arousal, attention, an

35 A clinical tool using readily available pretest variables discriminates such minimal-risk patients, for whom

36 evision consumption, nutritional status, and other relevant variables on individual preferences.

37 on, which uses repeated internal cross-validation to select variables and estimate coefficients in the presence of collin

38 In regression analyses, models comprising significant variables, but not the variables themselves, predicted involv

39 tions between the aortic diameter and any of the stratified variables were found (all p > 0.05).

40 (ii) We identified the variables that impact Env's glycosylation profile at sites wi

41 lyses, models comprising significant variables, but not the variables themselves, predicted involvement/non-involvement i

42 ondrial DNA haplogroups were not associated with any of the variables.

43 both are equally important and complementary thermodynamic variables.

44 Introducing these variables to a logistic regression analysis showed areas unde

45 All of these variables should be considered when evaluating possible labor

46 We conclude that these variables are not useful risk factors to measure to predict p

47 ty risk was explained by multivariable modelling with these variables; however, the remainder was unexplained.

48 Analyses of multiple ultrastructural variables revealed four organizational features.

49 tical depth (AOD) data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM2.5

50 We used survey design and weighting variables to produce national estimates of annual adult emerg