ライフサイエンスコーパス: prediction rule

1 dentified in the validation cohort using the prediction rule.

2 ulation depends on a patient's status on the prediction rule.

3 Further studies are needed to confirm the prediction rules.

4 rtional hazards models and standard clinical prediction rules.

5 development, validation, and use of clinical prediction rules.

6 sses (1) the development and use of clinical prediction rules, (2) the European Respiratory Society T

7 Our objective was to develop a practical prediction rule able to identify patients with GNB infec

8 Currently available prediction rules aiming to identify preschool children h

9 ms of VTE as well as the utility of clinical prediction rules and D-dimer testing in the diagnosis of

10 diction rules; important differences between prediction rules and decision rules; how to assess the p

11 Clinical prediction rules and models are developed by applying st

12 n the development and validation of clinical prediction rules and models.

13 In radiology, clinical prediction rules are an important method for determining

14 Prediction rules are currently created more frequently,

15 Three validated clinical prediction rules are described for adult and pediatric p

16 Clinical prediction rules are multifactorial tools used to aid in

17 Clinical prediction rules are used, but accuracy varies with stud

18 eding or ischemic events 1 year after PCI, a prediction rule assessing late ischemic and bleeding ris

19 The prediction rule assigned 1 point each for myocardial inf

20 The prediction rule assigns points based on age and the pres

21 We developed and validated a clinical prediction rule based on a set of electrocardiographic c

22 y can be estimated using a simple 4-variable prediction rule based on age, sex, smoking, and diabetes

23 to assess the potential clinical impact of a prediction rule before translating it into a decision ru

24 Our prediction rule can be used to estimate prN2/3 in patien

25 The spinal manipulation clinical prediction rule can be used to improve decision making f

26 Our prediction rule can be used to plan surveillance of new

27 nose strep throat, a well-validated clinical prediction rule can be useful and can help physicians ma

28 ow likelihood of significant stenoses, these prediction rules can help to substantially reduce health

29 ents were examined according to the clinical prediction rule criteria (symptom duration, symptom loca

30 The prediction rule described here accurately identifies pat

31 on of wheeze at preschool age, (3) published prediction rules developed to identify preschool childre

32 The clinical prediction rule discriminated between patients with and

33 Clinical prediction rules do not incorporate real-time incidence

34 A clinical prediction rule, followed by PCR screening, could be use

35 We derived a prediction rule for 1-year ischemic stroke risk post-TIA

36 onstruct a parsimonious model and a clinical prediction rule for 10-year all-cause mortality.

37 Results were compared with a previous prediction rule for all adults.

38 A prediction rule for asthma in preschool children might h

39 designed to devise and validate a practical prediction rule for atrial fibrillation/atrial flutter (

40 The prediction rule for children aged 2 years and older (nor

41 In the validation population, the prediction rule for children younger than 2 years (norma

42 for mild head injury can now be guided by a prediction rule for clinically important traumatic brain

43 e present prospective study was to develop a prediction rule for delirium in a cardiac surgery cohort

44 escribe the development of a simple clinical prediction rule for estimating the risk of NFI occurrenc

45 A prediction rule for P(LA) from DT(D) was developed in 50

46 A clinical prediction rule for patients with a score greater than 0

47 New risk factors and a prediction rule for postthoracotomy atrial fibrillation

48 ospectively derived and validated a clinical prediction rule for recurrent CDI that is simple, reliab

49 This study aimed to develop a prediction rule for recurrent CDI using the above deriva

50 ed predictive ability of the CHA(2)DS(2)VASc prediction rule for stroke and death in a nonanticoagula

51 To date, no clinical prediction rule for TB risk exists for use as a guide du

52 A recent study introduces a validated prediction rule for use in mild CHI, to limit the number

53 s study was to develop and validate clinical prediction rules for bacteremia and subtypes of bacterem

54 We derived and validated age-specific prediction rules for ciTBI (death from traumatic brain i

55 g data mining, and decision trees to produce prediction rules for functional class.

56 Most clinical prediction rules for percutaneous coronary intervention

57 Reported prediction rules for postoperative AF have suffered from

58 oo enthusiastic acceptance of it to evaluate prediction rules for primary prevention of cardiovascula

59 Clinical prediction rules for severe CAP do not appear adequately

60 he most recent literature regarding clinical prediction rules for the use of cranial computed tomogra

61 , an important next step would be to develop prediction rules for use in clinical practice, so that o

62 The introduction of several prediction rules has helped to guide clinicians in the u

63 Low-risk clinical prediction rules have been developed but need to be furt

64 Several clinical prediction rules have been developed to aid the clinicia

65 c hepatitis B (CHB), but previously proposed prediction rules have shown limited external validity.

66 These validated prediction rules identified children at very low risk of

67 Delirium prediction rules identify patients at risk for delirium

68 ds of evidence for developing and evaluating prediction rules; important differences between predicti

69 udy validates the Bacterial Meningitis Score prediction rule in the era of conjugate pneumococcal vac

70 Prospective validation of a clinical prediction rule in this population is warranted.

71 A simple prediction rule incorporating determinants of 30-day mor

72 A clinical prediction rule is being developed.

73 The kTSP prediction rule is the aggregation of voting among such

74 This prediction rule may help physicians make more rational d

75 mechanism of injury, suggests that low-risk prediction rules may be safely utilized by prehospital p

76 Clinical prediction rules, most notably the PECARN rules, can be

77 The derived prediction rule ranged from -2 to +2 with higher scores

78 Conjoint PRIM prediction rules recover approximately twice as many dif

79 We divided clinical prediction rule scoring into 4 tiers.

80 To insure generalizability, clinical prediction rules should also be validated in subjects di

81 To be useful, clinical prediction rules should be clinically important, have fa

82 found that the previously published suicide prediction rule significantly predicted post-deployment

83 Clinical prediction rules, sometimes called clinical decision rul

84 The clinical prediction rule stratified patients into mortality risk

85 The prediction rule stratified patients into risk groups wit

86 We present a simple echocardiographic prediction rule that accurately defines PH hemodynamics,

87 eatment decisions may be aided by a clinical prediction rule that identifies residents at low and hig

88 and c) to develop and internally validate a prediction rule that may be used in the emergency depart

89 h community-acquired pneumonia, we derived a prediction rule that stratifies patients into five class

90 h cancer-associated VTE to derive a clinical prediction rule that stratifies VTE recurrence risk.

91 ignificant predictors of AF and to develop a prediction rule that was evaluated through jackknifing.

92 ing the different representations DMP learnt prediction rules that were more accurate than default at

93 of a decline from baseline was compared to a prediction-rule that uses HBsAg levels of <1,500 IU/mL a

94 essed the predictive characteristics of four prediction rules (the original and revised American Thor

95 We previously developed a clinical prediction rule, the Bacterial Meningitis Score, that cl

96 We derived a simple echocardiographic prediction rule to allow hemodynamic differentiation of

97 We developed a robust prediction rule to assist clinicians in identifying pati

98 risk, we developed and validated a clinical prediction rule to determine the risk of violent offendi

99 characteristics were used to choose the best prediction rule to identify patients with Q fever pneumo

100 evalence of Q fever pneumonia and to build a prediction rule to identify patients with Q fever pneumo

101 old, and to develop and validate a clinical prediction rule to predict the risk of lymph node metast

102 1: Clinicians should use validated clinical prediction rules to estimate pretest probability in pati

103 d tomography (CT) imaging risks in children, prediction rules to guide decisions on CT scan use, and

104 (2) Create a clinical prediction rule using geriatric markers from preoperativ

105 In a population-based cohort, the score on a prediction rule using out-of-hospital factors was signif

106 In unsupervised analysis, a prediction rule was built from the expression profiles o

107 A prediction rule was created; patients were categorized i

108 from 11 countries (August 2009-May 2014), a prediction rule was derived stratifying patients into gr

109 The clinical prediction rule was developed by multivariate logistic r

110 A clinical prediction rule was developed on the training set, and v

111 A clinical prediction rule was developed using penalized logistic r

112 A clinical prediction rule was generated.

113 The prediction rule was used to stratify patients into group

114 Using the prediction rule we defined three risk categories for AF:

115 The prediction rule we describe accurately identifies the pa

116 k of deep vein thrombosis (DVT) by the Wells prediction rule were performed, and levels of fibrin deg

117 The following prediction rules were developed: The absence of severe c

118 Prediction rules were formulated by using multiple logis

119 0.75, respectively), but again, none of the prediction rules were particularly good.

120 0.68 and 0.74, respectively) but none of the prediction rules were particularly good.

121 viously derived and validated STONE clinical prediction rule, which includes five elements: sex, timi

122 ysis, clinicians cannot know whether using a prediction rule will be beneficial or harmful.

123 arch Network (PECARN) derived 2 age-specific prediction rules with 6 variables for clinically importa