ライフサイエンスコーパス: AUC

1 AUC results of our method on all datasets are above 0.8.

2 omial kernel and obtained accuracy of 76.0%, AUC 0.739, and F 1 score (macro weighted) 0.760 with Mon

3 ata (sensitivity, 91.4%; specificity, 96.0%; AUC, 0.94; PPV, 67.3%).

4 21) to 40 weeks (AOR, 6.1; 95% CI, 3.4-11.0; AUC, 0.799).

5 h hepatic veno-occlusive disease (P = .0053, AUC = 0.80).

6 oScore (OncoScore cut-off threshold = 21.09; AUC = 90.3%, 95% CI: 88.1-92.5%), indicating that OncoSc

7 at 40 weeks' PMA (AOR, 1.5, 95% CI, 1.0-2.1; AUC, 0.740) were only marginally below their peak values

8 s yielded an optimal cut-off value of 2.245 (AUC=0.999, p<0.001, sensitivity=0.983, specificity=1.00)

9 The independent test accuracy was 68.3%, AUC 0.692, F 1 score 0.676.

10 ischemic core threshold remained rCBF <30% (AUC, 0.83; 95% CI, 0.77, 0.85).

11 hose with poor outcomes (GOS-E, </=4 vs >4) (AUC = 0.771 and 0.777, respectively).

12 ed from 34 weeks (AOR, 1.8; 95% CI, 0.9-3.4, AUC, 0.721) to 40 weeks (AOR, 6.1; 95% CI, 3.4-11.0; AUC

13 heral endothelial dysfunction (70.9 +/- 5.6% AUC of normoxic control; all P < 0.05).

14 at 37 weeks' PMA (AOR, 1.8; 95% CI, 1.3-2.6; AUC, 0.743).

15 (Glasgow Outcome Scale-Extended GOS-E, 7-8) (AUC = 0.663 and 0.658, respectively) and identified thos

16 the experts (AUC, 0.77 [95% CI, 0.69-0.89]; AUC, 0.8 [95% CI, 0.72-0.88]) and the SVM (0.79 [0.71-0.

17 d that the cutoff point of FENO was 37.8ppb (AUC=0.647, sensitivity 83.3%, specificity 55.6%, p=0.007

18 based approach (accuracy 85.5% versus 72.9%, AUC 0.854 versus 0.733, F 1 score 0.854 versus 0.725; P

19 E-Ophtha databases achieved a 0.94 and 0.95 AUC score, respectively.

20 Our model achieved a 0.97 AUC with a 94% and 98% sensitivity and specificity, resp

21 improved significantly when Abeta42:Abeta40 (AUCs, 0.93-0.95; P </= .01), Abeta42 to total tau (T-tau

22 gues for its competitive predictive ability (AUC of 0.80+/-0.18, AUPR of 0.84+/-0.28) in inferring pr

23 3 macular pathologies with a mean accuracy (AUC) of 0.94 (range, 0.91-0.97), a mean precision of 0.9

24 Clinical BI-RADS was more accurate (AUC, 0.68; 95% CI: 0.63, 0.74) than Volpara (AUC, 0.64;

25 Although the actual AUC score is rather low, many of the predicted epitopes

26 inatory capacity for ATTR V122I amyloidosis (AUC = 0.97; 95% CI, 0.93-1.00), whereas a 4-parameter mo

27 An AUC of 0.73 was obtained when comparing healthy donors w

28 C) of 0.81, while EMR data alone achieved an AUC of 0.75.

29 itivity of 96%, a specificity of 84%, and an AUC of 0.97.

30 e upper EMA acceptance criteria limit for an AUC of 111.11%.

31 We generated an AUC for combining perforin and CD107a tests by creating

32 uded variables from all three domains had an AUC of 0.935.

33 after liver transplantation score obtains an AUC-ROC of 0.638 (95% CI, 0.632-0.645).

34 lticenter, randomized controlled trial of an AUC-based educational intervention aimed at reducing rA

35 study sought to investigate the impact of an AUC-based educational intervention on outpatient TTE ord

36 kers highly accurately predicted tPE with an AUC (95% CI) of 0.987 (0.961-1.000), sensitivity 100% an

37 eys demonstrated good discrimination with an AUC of 0.70.

38 g cognitive impairment was predicted with an AUC of 0.88 (95% CI 0.79-0.94) and a negative predictive

39 ent of SRF were also highly accurate with an AUC of 0.92 (range, 0.86-0.98), a mean precision of 0.61

40 ifferent ancestry from non-eQTL SNVs with an AUC of 0.939.

41 ely classify TB at chest radiography with an AUC of 0.99.

42 rage prediction accuracy is limited, with an AUC score (area under the receiver operating characteris

43 for end-stage liver disease score yields an AUC-ROC of 0.764 (95% CI, 0.756-0.771), whereas survival

44 tly lower specificity (0.26 [0.18-0.34]) and AUC (0.59 [0.55-0.64]).

45 el, yielded a better C statistic of 0.66 and AUC of 0.69 for 1-year mortality.

46 serious neurosensory impairment, the AOR and AUC at 40 weeks' PMA (AOR, 1.5, 95% CI, 1.0-2.1; AUC, 0.

47 yield of 68.3%, and 4-fold higher Cmax and AUC over the slow-releasing ATRA formulation.

48 AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characterist

49 ition, the individual variances of C max and AUC were much lower after GP administration.

50 e original HCT-CI had better C statistic and AUC estimates compared with the AML comorbidity index fo

51 The association of TMTV0 and AUC with metabolic response after 4 cycles, as well as p

52 T2 improved the predictive value of the API (AUC=0.70, 95% CI 0.56-0.84), but had also significant pr

53 (AUC, 0.66; 95% CI: 0.60, 0.72), dense area (AUC, 0.66; 95% CI: 0.60, 0.72), volumetric percentage of

54 alysed on Illumina 450 K methylation arrays (AUC = 0.70; sensitivity = 40.6%; specificity = 100%).

55 easured by a contemporary sensitivity assay (AUC, 0.909).

56 [AUC], 0.92) compared with the newer assays (AUCs, 0.87-0.89; P </= .01).

57 cal models yielded similar performance (best AUC: 0.73; 95% CI: 0.60, 0.86).

58 arger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.

59 herapy ([cisplatin 75 mg/m(2) or carboplatin AUC 5-6 plus pemetrexed 500 mg/m(2)] every 3 weeks for f

60 ed tomography-positive from -negative cases (AUC = 0.921, 0.923, and 0.646, respectively).

61 under the receiver operating characteristic (AUC) was also high for DNI (0.622, 95% CI 0.558-0.687, P

62 c curves revealed that loop characteristics (AUC = 0.99, 1.00 and 1.00; all P < 0.01) were better abl

63 oil was linearly related to the chylomicron AUC and Cmax values for alpha-carotene, lycopene, phyllo

64 re minor linear increases in the chylomicron AUC for lutein and alpha- and total tocopherol.

65 nterindividual rank order of the chylomicron AUCs was consistent across the carotenoids and fat-solub

66 for example, the Beck et al. classification AUC from 0.59 to 0.75 combining proportion of teeth with

67 ristic curve (AUC), was comparable for cMyC (AUC, 0.924), hs-cTnT (AUC, 0.927), and hs-cTnI (AUC, 0.9

68 In the derivation cohort, AUC was 0.84 (95% confidence interval [CI], .72-.95) and

69 e over half-maximal effective concentration (AUC:EC50) of 108.6 is estimated to result in a negative

70 the different areas and their corresponding AUCs.

71 pared with the standard diagnostic criteria (AUC < 0.67).

72 rt revision of the appropriate use criteria (AUC) for coronary revascularization.

73 , 0.924), hs-cTnT (AUC, 0.927), and hs-cTnI (AUC, 0.922) and superior to cTnI measured by a contempor

74 s comparable for cMyC (AUC, 0.924), hs-cTnT (AUC, 0.927), and hs-cTnI (AUC, 0.922) and superior to cT

75 the receiver operating characteristic curve (AUC) >0.8, and the inclusion of additional features offe

76 the receiver operating characteristic curve (AUC) analysis.

77 the receiver operating characteristic curve (AUC) as a metric to measure the precision-recall trade-o

78 the receiver operating characteristic curve (AUC) is used to compare the performance of different mac

79 the receiver operating characteristic curve (AUC) of 0.53 (95% CI: 0.44-0.78).

80 the receiver operating characteristic curve (AUC) of 0.807, and discriminate cis-eQTL SNVs shared acr

81 the receiver operating characteristic curve (AUC) of 0.91 with the brain window.

82 the receiver operating characteristic curve (AUC) of 0.92.

83 the receiver-operator characteristic curve (AUC) of UMT (0.84) was not significantly different from

84 the receiver operating characteristic curve (AUC) was computed.

85 der receiver operating characteristic curve (AUC) were calculated.

86 the receiver operating characteristic curve (AUC) were identified.

87 the receiver operating characteristic curve (AUC), and clinical impact was determined by decision cur

88 the receiver-operating characteristic curve (AUC), was comparable for cMyC (AUC, 0.924), hs-cTnT (AUC

89 the receiver-operating-characteristic curve (AUC).

90 the receiver operating characteristic curve (AUC).

91 the receiver operating characteristic curve (AUC).

92 the receiver operating characteristic curve (AUC, in g/mL. min) for (18)F-FTT was assessed in normal

93 he receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 < 0.001).

94 e Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).

95 the area under the receiver operating curve (AUC) and its significance was compared with a random gue

96 The highest area under the ROC curve (AUC) for steatosis of PNPLA3 rs738409 genotype, 8 protei

97 ceiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.

98 ceiver operating characteristic (ROC) curve (AUC), number of false discoveries and statistical power.

99 n time ( 0.44h), lower area under the curve (AUC) (33mugh/mL) and high clearance (916mL/h/kg) and low

100 cumulative viral load area under the curve (AUC) (P = .054).

101 ith 98.7% sensitivity (area under the curve (AUC) = 0.98).

102 Area under the curve (AUC) analysis was used for comparison of single and comb

103 was assessed using the area under the curve (AUC) and Brier score.

104 curve, and calculated area under the curve (AUC) and diagnostic parameters at optimal threshold.

105 nd fat-soluble vitamin area under the curve (AUC) and maximum content in the plasma chylomicron fract

106 the standards with an area under the curve (AUC) between 0.83 and 0.96 (P < 0.001).

107 mer disease have given area under the curve (AUC) estimates of <80%.

108 ad the highest AOR and area under the curve (AUC) for all outcomes.

109 teristic curve and the area under the curve (AUC) metrics on manually curated datasets confirmed the

110 The area under the curve (AUC) obtained with SPT was not significantly different f

111 aled a reasonably high area under the curve (AUC) of 0.91 for ApoA1.

112 l performance, with an area under the curve (AUC) of 0.92.

113 nstrated accuracy with area under the curve (AUC) of 1.000, 0.916, and 1.000, respectively, for discr

114 olon cancer with an area of under the curve (AUC) of 68.5%, in comparison to that of CEA at 83.6%.

115 utcome measure was the area under the curve (AUC) of cumulative pain scores from end of surgery to 6

116 ed to NTM, LTM reduced area under the curve (AUC) of FPD lesion scores during d21-42, HTM reduced the

117 disease onset with an area under the curve (AUC) of more than 0.85 in both the discovery (95% CI 0.8

118 ct to calibration, and area under the curve (AUC) of receiver operating characteristic curves.

119 We used the area under the curve (AUC) of the receiver operating characteristic (ROC) curv

120 The area under the curve (AUC) of the resulting probability maps was very high for

121 istics, the calculated area under the curve (AUC) to differentiate glaucoma was 0.873 for BMO-MRA, co

122 dependent and yielded area under the curve (AUC) values ranging from 0.75 to 0.94.

123 Area under the curve (AUC) was 0.984 for DCEMRI+HB phase vs. 0.934 for DCEMRI

124 sitivity, specificity, area under the curve (AUC), AHI, Epworth Sleepiness Scale (ESS) scores, blood

125 sitivity, specificity, area under the curve (AUC), and positive predictive value (PPV) of the revised

126 Area under the curve (AUC), describing the discriminatory/predictive performan

127 ing the time-dependent area under the curve (AUC), predicting 5-year risk; internal validation was pe

128 Based on area under the curve (AUC), the range of liver TCA levels spanned nearly an or

129 ife and area under concentration-time curve (AUC) of tanshinones, with the plasma AUC of CTS, TSI and

130 and area under the concentration-time curve (AUC) were compared, and average bioequivalence (ABE) was

131 1 was 583.1 pg/mL with the area under curve (AUC) 0.76 (95% CI, 0.70-0.82; P < 0.001), sensitivity 78

132 ween HBV and HCV patients (area under curve (AUC) 0.82 for HCC, 0.90 for non-HCC).

133 e points and calculated to Area Under Curve (AUC) for 1 year postoperatively.

134 sis revealed a significant area under curve (AUC) value only for miR-455-3p in the serum (AUROC = 0.7

135 nsitivity, specificity and area under curve (AUC) were observed when comparing dwgLASSO with conventi

136 the receiver-operating-characteristic curve (AUCs) of 0.78 compared with 0.58 in the SUVmax model.

137 acteristic curves and areas under the curve (AUCs) were used to assess model performance by using the

138 the receiver operating characteristic curve [AUC] = 0.93, P < .001) and MCF (AUC = 0.92, P < .001) ca

139 the receiver operating characteristic curve [AUC]) between individuals with and without lung cancer (

140 the receiver operator characteristic curve [AUC], 0.79; 95% confidence interval [CI], 0.74-0.83).

141 the receiver operating characteristic curve [AUC], 0.92) compared with the newer assays (AUCs, 0.87-0

142 the receiver operating characteristic curve [AUC], 1) for the patient cohort.

143 the receiver operating characteristic curve [AUC]: 0.96 and 0.89, respectively) for separation betwee

144 rea under the sensitivity-specificity curve [AUC] of 0.94) and in a blinded verification set (AUC of

145 was highest for ONSD (area under the curve [AUC] 0.91, 95% CI 0.88-0.95).

146 (derivation cohort ROC-area under the curve [AUC] 0.97 [95% CI 0.95-0.98]), HMGB1 (0.95 [0.93-0.98]),

147 ion for low-dose RCEM (area under the curve [AUC] 24 968) compared with low-dose (AUC-38 225) or stan

148 erating characteristic area under the curve [AUC], 0.74; 95% confidence interval [95% CI], 0.61 to 0.

149 was highest for NEWS (area under the curve [AUC], 0.77; 95% confidence interval [CI], 0.76-0.79), fo

150 y associated with pCR (area under the curve [AUC], 0.85).

151 atients was rCBF <20% (area under the curve [AUC], 0.89; 95% CI, 0.84, 0.94), whereas in alteplase co

152 e diagnostic accuracy (area under the curve, AUC).

153 re (area under the concentration-time curve; AUC).

154 er receiver operating characteristic curves (AUC) and sensitivities at fixed specificities of vessel

155 the receiver-operator characteristic curves (AUC) were used to evaluate age-stratified models in seco

156 he receiver operating characteristic curves (AUCs) were calculated to evaluate performance in determi

157 he receiver operating characteristic curves (AUCs).

158 he receiver operating characteristic curves [AUCs] and positive predictive values [PPVs].

159 erial infection (naive AUC = 0.94; nested CV-AUC = 0.86).

160 0.744, P<0.0001) but not in validation data (AUC=0.749 versus AUC=0.747, P=0.785).

161 60, 0.72), volumetric percentage of density (AUC, 0.64; 95% CI: 0.58, 0.70), and density volume (AUC,

162 ontinuous measures of percentage of density (AUC, 0.66; 95% CI: 0.60, 0.72), dense area (AUC, 0.66; 9

163 modifications in the methods for developing AUC, most notably, alterations in the nomenclature for a

164 ntration, retained excellent discrimination (AUC = 0.92; 95% CI, 0.86-0.99).

165 o cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88).

166 curve [AUC] 24 968) compared with low-dose (AUC-38 225) or standard-dose NCEM (AUC-38 685), P < .05.

167 erentiating between KRAS-positive and EGFR+ (AUC = 0.65, FDRNoether = 0.05).

168 uced a miRNA algorithm for diagnosis of EOC (AUC 0.90; 95% CI: 0.81-0.99).

169 h high applicability concerns were excluded (AUC, 0.931; SE, 0.020).

170 rating characteristic curve for the experts (AUC, 0.77 [95% CI, 0.69-0.89]; AUC, 0.8 [95% CI, 0.72-0.

171 imilarly, including intraoperative features (AUC = 0.82; 95% CI: 0.66-0.94) in ARF prediction improve

172 oved performance over preoperative features (AUC = 0.72; 95% CI: 0.50-0.85), though not significantly

173 ssociated with the binary semantic features (AUC = 0.56-0.76).

174 al was 84.65%-118.13% and 80.00%-125.00% for AUC and Cmax, respectively.

175 MCC, 4.18-21.47% for ST and 0.013-0.131 for AUC.

176 es (25th and 75th percentiles (p25-p75)) for AUC at 1 year were as follows: 349 (337-351) for LC and

177 mus metabolite 13-O-desmethyl tacrolimus for AUC, but it failed the EMA criterion.

178 .08, respectively) and LV ejection fraction (AUC = 0.56, 0.69 and 0.69; all P > 0.05) to distinguish

179 niparib, significantly reduced the (18)F-FTT AUC in the spine (median difference before and after tre

180 gene mutation detection in low-grade glioma (AUC, 0.818) and MTI in high-grade glioma (AUC, 0.854) an

181 a (AUC, 0.818) and MTI in high-grade glioma (AUC, 0.854) and for all WHO grades (AUC, 0.899) among al

182 glioma (AUC, 0.854) and for all WHO grades (AUC, 0.899) among all biomarkers.

183 ed the best separation between these groups (AUC = 0.99).

184 gnificance was compared with a random guess (AUC = 0.5) using the Noether test.

185 d receiver operator curves of the miRNAs had AUCs of 91 to 100%.

186 cirrhotic HBV patients with and without HCC (AUC 0.503) or HCV patients with and without HCC (AUC 0.6

187 0.503) or HCV patients with and without HCC (AUC 0.63).

188 A high AUC in cycle 1 (>/=9400 mg x h per liter) was associated

189 develop eczema at 3 months of age was high (AUC: 0.91, 95% CI: 0.84-0.98).

190 PUDTI achieved the highest AUC among the 7 methods on all 4 datasets.

191 emporal inferior location showed the highest AUC.

192 formation of an active oligomeric state: (i) AUC data demonstrate the presence of oligomers; (ii) mut

193 e was no significant difference (p > 0.2) in AUC for BPE quantified from the three post-contrast sequ

194 cTnI (but not hs-cTnI) led to an increase in AUC to 0.931 (P<0.0001) and 0.926 (P=0.003), respectivel

195 l improvement ( approximately 2% increase in AUC).

196 ge, transfusion was followed by increases in AUC for serum iron (P < 0.01), transferrin saturation (P

197 d cardiac MR imaging resulted in an inferior AUC of 0.863 (P < .01).

198 nts in synonymous (AUC = 0.88) and intronic (AUC = 0.83) public datasets, dismissing benign variants

199 strong enough to differentiate keratoconus (AUC > 0.9).

200 mal and hyperplasia from high-grade lesions (AUC > 0.94).

201 autodegration pathway of a ubiquitin ligase (AUC = 0.69, p<10(-4)).

202 The maximum cumulative viral load AUC was the best predictor of early (days 0-100) and lat

203 as carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88).

204 e lead measurement for the diagnosis of LVH (AUC: 0.80; p < 0.001).

205 istic curve [AUC] = 0.93, P < .001) and MCF (AUC = 0.92, P < .001) can be used to discriminate patien

206 rmined by the area under the ROC curve (Mean AUC = 0.722).

207 n AUC = 0.92) fusion of all modalities (mean AUC = 0.54) and individual modalities (mean AUC = 0.90,

208 AUC = 0.54) and individual modalities (mean AUC = 0.90, 0.53, 0.71, 0.73, 0.62, 0.68).

209 r each classification task outperforms (mean AUC = 0.92) fusion of all modalities (mean AUC = 0.54) a

210 interval [CI], 0.76-0.79), followed by MEWS (AUC, 0.73; 95% CI, 0.71-0.74), qSOFA (AUC, 0.69; 95% CI,

211 Using a predictive model (AUC = 0.77), physician preference largely determined whi

212 LCD yielded better performance of the model (AUC: 0.73; 95% CI: 0.60, 0.87]).

213 2 groups were pRNFL (AUC, 0.956) and mRNFL (AUC, 0.906).

214 he best discriminators for an EGFR mutation (AUC 0.77 and 0.87, respectively).

215 ction was more difficult for PMS2 mutations (AUC, 0.64; 95% CI, 0.60 to 0.68) than for other genes.

216 velength analytical ultracentrifugation (MWL-AUC) experiments, demonstrating improvement in both the

217 or discriminating bacterial infection (naive AUC = 0.94; nested CV-AUC = 0.86).

218 low-dose (AUC-38 225) or standard-dose NCEM (AUC-38 685), P < .05.

219 FR mutation status (EGFR+ vs. EGFR-negative, AUC = 0.67, FDRNoether = 0.0032), as well as differentia

220 ion status (KRAS-positive vs. KRAS-negative, AUC = 0.50-0.54).

221 erican Heart Association guidelines, the new AUC for coronary artery revascularization were separated

222 d a statistically significant improvement of AUC values compared with the ONSD method alone (0.93, 95

223 The hardware is an extension of the Open AUC MWL detector developed in academia and first introdu

224 for rituximab dose needed to obtain optimal AUC according to TMTV0 was constructed, and the 375 mg/m

225 Important univariate predictors of outcome (AUC range, 0.66-0.70) were dimensional measures of mania

226 lso significant predictive value on its own (AUC=0.65, 95% CI 0.52-0.79).

227 centration, heat-induced increase in partial AUCs and post-treatment residual content of nicotine in

228 0; all P < 0.01) were better able then peak (AUC = 0.75, 0.89 and 0.76; P = 0.06, <0.01 and 0.08, res

229 MTI had the highest diagnostic performance (AUC, 0.782) for differentiation between gliomas of grade

230 icant difference in the overall performance (AUC, 0.930 vs. 0.891; 2-tailed P value for difference, 0

231 curve (AUC) of tanshinones, with the plasma AUC of CTS, TSI and TSA in GP 5-184, 4-619 and 5-130 tim

232 patient readmissions were more predictable (AUC 0.84) with more preventable causes, whereas younger

233 al case-control series and show a predictive AUC of 84%.

234 ssions were difficult to predict or prevent (AUC 0.65).

235 iscriminate between the 2 groups were pRNFL (AUC, 0.956) and mRNFL (AUC, 0.906).

236 MEWS (AUC, 0.73; 95% CI, 0.71-0.74), qSOFA (AUC, 0.69; 95% CI, 0.67-0.70), and SIRS (AUC, 0.65; 95%

237 High quality (AUC > 0.7) was achieved for approximately 200 kinases by

238 upon combination of both mediators reaching AUC=1.

239 ability maps was very high for both readers, AUC = 0.99 (SD = 0.05).

240 As a result, GrwLDA obtained reliable AUCs of 0.9449, 0.8562, and 0.8374 for overall, novel ln

241 In prior coronary revascularization AUC documents, indications for revascularization in acut

242 en with the prior coronary revascularization AUC, revascularization in clinical scenarios with ST-seg

243 -risk from lower-risk coronary arteries (ROC AUC: 0.76; 95% CI: 0.62 to 0.91; p = 0.002); however, my

244 ng characteristics area under the curve [ROC AUC]: 0.86; 95% CI: 0.80 to 0.92; p < 0.0001), and corre

245 del, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC-AUC, 0.740) cohorts.

246 he Danish (ROC-AUC, 0.739) and Scottish (ROC-AUC, 0.740) cohorts.

247 ceiver operating characteristics curves (ROC-AUCs).

248 , p=0.007) and that of R5 was 3.03cmH2O/L/S (AUC=0.684, sensitivity 72.2%, specificity 52.8%, p=0.001

249 p=0.001) and that of R20 was 2.77cmH2O/L/S (AUC=0.684, sensitivity 74.5%, specificity 59.4%, p=0.001

250 ll genes in malignant needle biopsy samples (AUC: 0.80 to 0.98), confirming previous findings in pros

251 lly distinguish ASD samples from TD samples (AUC is 0.88).

252 ASE-AF2, CHADS2, CHA2DS2VASc or HATCH score (AUC 0.716, 0.671, 0.648, 0.552, 0.519 and 0.583, respect

253 enerate a discriminatory genetic risk score (AUC=0.76 in our sample) that is significantly correlated

254 of 0.94) and in a blinded verification set (AUC of 0.92) to distinguish TB and non-TB samples.

255 odel improved the performance significantly (AUC: 0.87; 95% CI: 0.77, 0.96; Delong's P = 0.012).

256 the validation cohort, the score had similar AUC, Brier score, and estimated HRs.

257 FA (AUC, 0.69; 95% CI, 0.67-0.70), and SIRS (AUC, 0.65; 95% CI, 0.63-0.66) (P < 0.01 for all pairwise

258 g near-perfect prediction of its substrates (AUC = 0.974).

259 The 3rd-order RMS of the posterior surface (AUC: 0.928) obtained the highest discriminating capabili

260 athogenic and benign variants in synonymous (AUC = 0.88) and intronic (AUC = 0.83) public datasets, d

261 ), or Abeta42 to phosphorylated tau (P-tau) (AUCs, 0.94-0.95; P </= .001) ratios were used.

262 5; P </= .01), Abeta42 to total tau (T-tau) (AUCs, 0.94; P </= .05), or Abeta42 to phosphorylated tau

263 The AUC and sensitivity at 95% specificity of vessel densiti

264 The AUC and the Q* index were 0.916 (SE, 0.018) and 0.849, i

265 The AUC metric appears to be suitable for the evaluation of

266 The AUC of PERSEVERE-XP was superior to that of PERSEVERE.

267 AUC, 0.65; 95% CI: 0.59, 0.71), although the AUC differences were not statistically significant.

268 For all the nine host genera, the AUC is over 0.85 and for five of them, the AUC is higher

269 bination of miR-125a-3P and CEA improved the AUC to 85.5%.

270 ation time (5.5h) along with increase in the AUC (56mugh/mL) for CPD100 Li and (9.5h) with AUC (170mu

271 rage time (P < 0.001 for linear trend in the AUC of serum indirect bilirubin and iron levels).

272 mbalance problem, we novelly incorporate the AUC (area under the curve) score into the optimizing obj

273 with these data only minimally increased the AUC values for BE (to 0.799) and EAC (to 0.754).

274 g the best parameters of all 3 machines, the AUC of the Belin/Ambrosio enhanced ectasia total derivat

275 The primary objective of the AUC is to provide a framework for the assessment of prac

276 lesion scores during d21-42, HTM reduced the AUC of FPD lesion scores during d7-21 and d21-42.

277 ruste eyes, the ROC analysis showed that the AUC values of the mean K, thinnest pachymetry, ARTmax, B

278 e AUC is over 0.85 and for five of them, the AUC is higher than 0.98 when the word size is 6 indicati

279 The AUCs for the association of any and significant fibrosis

280 The AUCs of the pretrained models were greater than that of

281 The AUCs were 0.84 (95% CI 0.78-0.89) for deep learning meth

282 After internal validation, the AUCs decreased to 0.74 and 0.54, respectively.

283 WGRS97 improved the accuracy in training (AUC=0.782 versus AUC=0.744, P<0.0001) but not in validat

284 ed activity of RNA polymerase transcription (AUC = 0.62, p=0.03) and intensity dispersion was predict

285 scopy (TEM), Analytical Ultracentrifugation (AUC), and UV/Vis spectroscopy.

286 d not achieve any classification (validation AUC: 0.50; 95% CI: 0.36, 0.64).

287 but not in validation data (AUC=0.749 versus AUC=0.747, P=0.785).

288 d the accuracy in training (AUC=0.782 versus AUC=0.744, P<0.0001) but not in validation data (AUC=0.7

289 between converting vs nonconverting visits (AUC, 0.76; bootstrapped 95% CI, 0.71-0.82).

290 F), had better predictive ability for VLRAF (AUC 0.782) than the APPLE, ALARMc, BASE-AF2, CHADS2, CHA

291 AUC, 0.68; 95% CI: 0.63, 0.74) than Volpara (AUC, 0.64; 95% CI: 0.58, 0.70) and continuous measures o

292 64; 95% CI: 0.58, 0.70), and density volume (AUC, 0.65; 95% CI: 0.59, 0.71), although the AUC differe

293 A syndrome (AHI >/=30 and ESS score >10) was AUC 0.80 (95% CI, 0.78 to 0.82) and 0.83 (95% CI, 0.77 t

294 of 28) specificity (95% CI: 82%, 100%), with AUC of 0.81.

295 UC (56mugh/mL) for CPD100 Li and (9.5h) with AUC (170mugh/mL) for CPD100PEGLi.

296 S) and progression free survival (PFS), with AUC of 0.976 and 0.932, respectively.

297 ed well in predicting a good prognosis, with AUC of 0.767, 0.857 and 0.820, respectively.

298 ted with severe symptoms during DBPCFC, with AUCs of 0.70-073.

299 odels had the best overall performance, with AUCs >0.68 in the NLST and >0.77 in the PLCO.

300 ion discriminated well between infants with (AUC, 87%) and without (AUC, 80%) NEC.

301 between infants with (AUC, 87%) and without (AUC, 80%) NEC.