ライフサイエンスコーパス: receiver operating characteristic curve

1 ended (bivariate model or hierarchic summary receiver operating characteristic curve).

2 automatically, and so was the area under the receiver operating characteristic curve.

3 rhotic livers was assessed by area under the receiver operating characteristic curve.

4 sensitivity, specificity, and area under the receiver operating characteristic curve.

5 es of performance such as the area under the receiver operating characteristic curve.

6 was tested by calculating the area under the receiver operating characteristic curve.

7 Optimal implantation depths were defined by receiver operating characteristic curve.

8 d sensitivity and specificity metrics on the receiver operating characteristic curve.

9 performed by calculating the area under the receiver-operating characteristic curve.

10 itivity to be predicted were calculated with receiver operating characteristic curves.

11 DeLong method for statistical comparison of receiver operating characteristic curves.

12 We determined optimal thresholds using receiver operating characteristic curves.

13 no significant differences in area under the receiver operating characteristic curves.

14 All methods had similar areas under the receiver operating characteristic curves.

15 libration, and area under the curve (AUC) of receiver operating characteristic curves.

16 Density cutoffs were determined using receiver operating characteristic curves.

17 We calculated the area under the receiver-operating characteristics curve.

18 te gadolinium enhancement was compared using receiver operating characteristics curves.

19 d Early Warning Score (median area under the receiver operating characteristic curve 0.67), and highe

20 l Early Warning Score (median area under the receiver operating characteristic curve 0.71) and electr

21 ac Arrest Risk Triage (median area under the receiver operating characteristic curve 0.73).

22 reliability of our approach (area under the receiver operating characteristic curve 0.85).

23 te logistic regression (P = 0.04, area under receiver operating characteristic curve 0.89 (95% confid

24 edicted outcome on admission (area under the receiver operating characteristics curve 0.898 [95% CI 0

25 1; P=0.01) and triglycerides (area under the receiver-operating characteristic curve 0.740 versus are

26 acteristic curve 0.740 versus area under the receiver-operating characteristic curve 0.758; P<0.01).

27 sity lipoprotein cholesterol (area under the receiver-operating characteristic curve 0.806 versus 0.8

28 ed in the SRL had the highest area under the receiver operating characteristic curve (0.893 [95% CI,

29 .74) than a model using MELD (area under the receiver operating characteristic curve = 0.62) or MELD

30 urve = 0.62) or MELD and age (area under the receiver operating characteristic curve = 0.67) to predi

31 re had better discrimination (area under the receiver operating characteristic curve = 0.74) than a m

32 ed overall moderate accuracy (area under the receiver operating characteristic curve = 0.82).

33 lassifier for AMR was identified (area under receiver operating characteristic curve = 0.84; 95% conf

34 s with a similar reliability (area under the receiver operating characteristic curve = 0.973 [0.838-1

35 urrence in the next 48 hours (area under the receiver-operating characteristics curve = 0.88 +/- 0.07

36 ory response syndrome (median area under the receiver operating characteristic curve, 0.60) and Sepsi

37 lure Assessment score (median area under the receiver operating characteristic curve, 0.62), intermed

38 an Failure Assessment (median area under the receiver operating characteristic curve, 0.65) and Modif

39 PD diameter cutoff of 7.2 mm (area under the receiver operating characteristic curve, 0.70; 95% CI, 0

40 with colon-only CD (P = .001; area under the receiver operating characteristic curve, 0.72).

41 improved survival prediction (area under the receiver operating characteristic curve, 0.73 vs 0.60, r

42 LS-to-TLV ratio measurements (area under the receiver operating characteristic curve, 0.753) for diff

43 complications (overall model area under the receiver operating characteristic curve, 0.77).

44 erivation and pooled cohorts (area under the receiver operating characteristic curve, 0.81 vs 0.80; D

45 urve, 0.89) and at admission (area under the receiver operating characteristic curve, 0.82).

46 riple-negative breast cancer (area under the receiver operating characteristic curve, 0.834).

47 , 0.929) than splenic volume (area under the receiver operating characteristic curve, 0.835) or LLS-t

48 dation cohorts was excellent (area under the receiver operating characteristic curve, 0.84 both).

49 AD with dementia vs controls (area under the receiver operating characteristic curve, 0.87, which is

50 922 (59 of 64; kappa = 0.816; area under the receiver operating characteristic curve, 0.886 +/- 0.042

51 grade at treatment decision (area under the receiver operating characteristic curve, 0.89) and at ad

52 with PD and control subjects (area under the receiver operating characteristic curve, 0.92 and 0.88,

53 ignificantly higher accuracy (area under the receiver operating characteristic curve, 0.929) than spl

54 sitivity and 88% specificity (area under the receiver operating characteristic curve, 0.95).

55 thod at predicting ischemia (areas under the receiver-operating characteristic curves, 0.87 versus 0.

56 n the epicardial myocardium (areas under the receiver-operating characteristic curves, 0.87 versus 0.

57 t statistically significant (areas under the receiver-operating characteristic curves, 0.90 versus 0.

58 e best predictor of death/VT (area under the receiver-operating characteristics curve, 0.80); for eve

59 nary output after furosemide (area under the receiver-operating-characteristic curve, 0.75; 95% CI, 0

60 on of LNM (difference in the areas under the receiver-operating-characteristic curves, 0.139; 95% con

61 stently frequent" trajectory (area under the receiver operating characteristic curve: 0.84, sensitivi

62 radiography, as given by the area under the receiver operating characteristic curve (1.23-fold, P <

63 e cerebral blood flow values (area under the receiver operating characteristics curve: 63%-69%, false

64 erentiating ACR from non-ACR (area under the receiver operating characteristic curve = 90%, 95% confi

65 The area under the receiver operating characteristic curve (95% CI) of tiss

66 Area under the receiver-operating characteristic curve (95% CI) for the

67 Discriminatory value was assessed by using receiver operating characteristic curves.A total of 2882

68 images by using an alternative free-response receiver operating characteristic curve (AFROC) method.

69 Receiver operating characteristic curve analyses and pre

70 isons were performed in template space, with receiver operating characteristic curve analyses to asse

71 Receiver operating characteristic curve analyses were us

72 Binary logistic regression and receiver operating characteristic curve analyses were us

73 Correlation, Bland-Altman, and receiver-operating characteristic curve analyses were pe

74 Receiver-operating-characteristic curve analyses of the

75 -Altman), and classification (area-under-the-receiver-operating characteristic curve) analyses.

76 A receiver operating characteristic curve analysis (area u

77 preoperative biopsies were reported by using receiver operating characteristic curve analysis and Spe

78 The summary receiver operating characteristic curve analysis demonst

79 Receiver operating characteristic curve analysis evidenc

80 These results were confirmed in a receiver operating characteristic curve analysis perform

81 Receiver operating characteristic curve analysis showed

82 We conducted receiver operating characteristic curve analysis to dete

83 Conventional, frequentist receiver operating characteristic curve analysis was con

84 Receiver operating characteristic curve analysis was per

85 Receiver operating characteristic curve analysis was per

86 Receiver operating characteristic curve analysis was use

87 screening tests using a hierarchical summary receiver operating characteristic curve analysis when at

88 In a receiver operating characteristic curve analysis, an SUV

89 lly overt severe sepsis syndrome patients by receiver operating characteristic curve analysis, with a

90 es for patient outcome were determined using receiver operating characteristic curve analysis.

91 t, Fisher-exact test, Shapiro-Wilk test, and receiver operating characteristic curve analysis.

92 r and nonresponder patients was evaluated by receiver operating characteristic curve analysis.

93 8.7% and specificity was 77.3% at case-based receiver operating characteristic curve analysis.

94 ict malignant disease was determined using a receiver operating characteristic curve analysis.

95 DIT, and DIS plus DIT with a time-dependent receiver operating characteristic curve analysis.

96 Model discrimination was assessed using receiver operating characteristic curve analysis.

97 gingivalis (0.23%) and T. forsythia (0.35%), receiver operating characteristic curves analysis demons

98 Receiver-operating characteristic curve analysis reveale

99 Time-dependent receiver-operating characteristic curve analysis was per

100 Receiver-operating-characteristic curve analysis of the

101 Receiver operating characteristic curve and the area und

102 Receiver operating characteristic curves and areas under

103 d cross-validated models was evaluated using receiver operating characteristic curves and by calculat

104 We used Bayesian LCMs to generate unbiased receiver operating characteristic curves and found that

105 tion method was used to describe the summary receiver operating characteristics curve and bivariate m

106 rmance was assessed using the area under the receiver-operating characteristic curve and compared wit

107 e, likelihood ratio negative, area under the receiver operating characteristic curve, and by cross-va

108 crimination, expressed by the area under the receiver operating characteristic curve, and calibration

109 basis was assessed by using areas under the receiver operating characteristic curves, and difference

110 nstrate that our method has uniformly better receiver operating characteristic curves, and identifies

111 acy were quantified using multilevel models, receiver operating characteristic curves, and test sensi

112 yzed with mixed effects logistic regression, receiver operating characteristic curves, and the Fisher

113 tic regression models were used to construct receiver-operating characteristic curves, and predictor

114 ging alone was assessed, and areas under the receiver-operating-characteristic curves are presented.

115 The receiver operating characteristic curve area for the glo

116 At these time points, the 6MWD receiver operating characteristic curve-area under the c

117 Receiver operating characteristic curve areas trended lo

118 c accuracy as measured by the area under the receiver operating characteristic curve (AROC).

119 Receiver operating characteristic curves assessed the ac

120 sign of effective sgRNAs with area under the receiver operating characteristic curve (AUC) >0.8, and

121 had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60).

122 reening predicted tPE with an area under the receiver operating characteristic curve (AUC) (95% CI) =

123 ents with BE or EAC using the area under the receiver operating characteristic curve (AUC) analysis.

124 We used area under the receiver operating characteristic curve (AUC) as a metri

125 The area under the receiver operating characteristic curve (AUC) for REVEL

126 score of at least 7, and the area under the receiver operating characteristic curve (AUC) for the mo

127 Area under the receiver operating characteristic curve (AUC) is used to

128 tality prediction achieved an area under the receiver operating characteristic curve (AUC) of 0.53 (9

129 ng set with a cross-validated area under the receiver operating characteristic curve (AUC) of 0.807,

130 n cohort, PERSEVERE-XP had an area under the receiver operating characteristic curve (AUC) of 0.90 (9

131 city (95% CI: 76%, 92%), with area under the receiver operating characteristic curve (AUC) of 0.91 wi

132 ty in the test cohort with an area under the receiver operating characteristic curve (AUC) of 0.92.

133 7, and 8 log10 IU/mL with an area under the receiver operating characteristic curve (AUC) of 0.97, 0

134 Central reader area under the receiver operating characteristic curve (AUC) values wer

135 nd menopausal status, and the area under the receiver operating characteristic curve (AUC) was comput

136 score were analyzed, and the area under the receiver operating characteristic curve (AUC) was used t

137 Sensitivity, specificity, and area under receiver operating characteristic curve (AUC) were calcu

138 ayer and sector with the best area under the receiver operating characteristic curve (AUC) were ident

139 rformance for COPD detection: area under the receiver operating characteristic curve (AUC), 0.65 to 0

140 sensitivity and specificity (area under the receiver operating characteristic curve (AUC), 0.97, Man

141 mination was evaluated by the area under the receiver operating characteristic curve (AUC), and clini

142 male participants in terms of area under the receiver operating characteristic curve (AUC), sensitivi

143 the gold standard to compute area under the receiver operating characteristic curve (AUC).

144 terms of odds ratio (OR) and area under the receiver operating characteristic curve (AUC).

145 he GRSs was determined by the area under the receiver operating characteristic curve (AUC).

146 ty was evaluated by using the area under the receiver operating characteristic curve (AUC).

147 ormance was assessed with the area under the receiver operating characteristic curve (AUC).

148 arly warning score, using the area under the receiver operating characteristic curve (AUC).

149 The area under the receiver operating characteristic curve (AUC, in g/mL. m

150 predicts the outcome with an area under the receiver operating characteristic curve (AUC-ROC) value

151 Area under receiver operating characteristic curves (AUC) and sensi

152 ng had a statistically higher area under the receiver operating characteristics curve (AUC) than non-

153 was assessed by measuring the area under the receiver operating characteristics curve (AUC), sensitiv

154 for AMI, as quantified by the area under the receiver-operating characteristic curve (AUC), was compa

155 tures were compared using the area under the receiver-operating-characteristic curve (AUC).

156 thy controls (cross-validated area under the receiver operating characteristic curve [AUC] = 0.81).

157 predicted TR-ROP better than GA (area under receiver operating characteristic curve [AUC] = 0.82 vs.

158 Results LAS (area under the receiver operating characteristic curve [AUC] = 0.93, P

159 ed risks, (2) discrimination (area under the receiver operating characteristic curve [AUC]) between i

160 sensitivity, specificity, and area under the receiver operating characteristic curve [AUC]) of the nu

161 [18F]flutemetamol PET status (area under the receiver operating characteristic curve [AUC], 0.92) com

162 n low- and high-grade glioma (area under the receiver operating characteristic curve [AUC], 1) for th

163 gher discriminatory accuracy (area under the receiver operating characteristic curve [AUC]: 0.96 and

164 ed well in the TAMOF cohort (areas under the receiver operating characteristic curves [AUC], 0.84 [95

165 In addition, the averaged areas under the receiver operating characteristic curve (AUCs) achieved

166 d were used to determine the areas under the receiver operating characteristic curve (AUCs) and likel

167 tivities, specificities, and areas under the receiver operating characteristic curve (AUCs) of PET/CT

168 Descriptive statistics and areas under the receiver operating characteristic curves (AUCs) were cal

169 gm, using logistic model and areas under the receiver operating characteristic curves (AUCs).

170 The areas under the receiver operating characteristics curve (AUCs) were cal

171 c regression analysis showed areas under the receiver-operating-characteristic curve (AUCs) of 0.78 c

172 finition based on the highest area under the receiver operating characteristic curves [AUCs] and posi

173 discrimination was assessed using area under receiver operating characteristic curve (AUROC) and cali

174 ive-fold cross-validation and the Area Under Receiver Operating Characteristic Curve (AUROC) are empl

175 The area under the receiver operating characteristic curve (AUROC) for QSWP

176 termined by comparison of the area under the receiver operating characteristic curve (AUROC) for the

177 sis (stage 1 or more) with an area under the receiver operating characteristic curve (AUROC) of 0.82

178 imaging achieved a validated area under the receiver operating characteristic curve (AUROC) of 0.98,

179 ens), specificity (Spec), and area under the receiver operating characteristic curve (AUROC) values w

180 Area under the receiver operating characteristic curve (AUROC) values,

181 The area under the receiver operating characteristic curve (AUROC) was used

182 ta under the criterion of the area under the receiver operating characteristic curve (AUROC).

183 of baseline risk of death and area under the receiver operating characteristic curve (AUROC).

184 nation was assessed using the area under the receiver operating characteristic curve (AUROC).

185 ndard, and compared accuracy with area under receiver operating characteristic curves (AUROC).

186 sensitivity, specificity, and area under the receiver-operating characteristic curve (AUROC) of HBeAg

187 Area under the receiver operating characteristic curve based on ML (0.6

188 99, 0.94, and 0.95, respectively; P=0.19 for receiver operating characteristic curve comparison).

189 Receiver operating characteristic curves constructed usi

190 Receiver operating characteristic curves determined the

191 iagnostic sensitivity and specificity in the receiver operating characteristic curve did not differ b

192 Using receiver operating characteristic curves, diffusion char

193 were compared on the basis of nonparametric receiver operating characteristic curve estimations by u

194 Receiver operating characteristic curve for biomarkers s

195 Mean validation areas under the receiver operating characteristic curve for discriminati

196 The area under the receiver operating characteristic curve for distinguishi

197 Areas under the receiver operating characteristic curve for early and la

198 The area under the receiver operating characteristic curve for electronic C

199 The area under the receiver operating characteristic curve for internal and

200 and acute organ dysfunction and generated a receiver operating characteristic curve for plasma angio

201 nt comparison) and performed equally well on receiver operating characteristic curve for predicting a

202 leep quality for concurrent validity and the receiver operating characteristic curve for predictive v

203 The area under the receiver operating characteristic curve for screening be

204 According to the area under the receiver operating characteristic curve for SUVpeak, DIB

205 emic total perfusion deficit areas under the receiver operating characteristic curve for the 2 expert

206 The area under the receiver operating characteristic curve for the diagnosi

207 The area under the receiver operating characteristic curve for the differen

208 Similarly, for non-AC data, areas under the receiver operating characteristic curve for the experts

209 The receiver operating characteristic curve for the full reg

210 We examined the area under the receiver operating characteristic curve for the model wi

211 Mean validation area under the receiver operating characteristic curves for discriminat

212 The area under the receiver operating characteristics curve for the composi

213 The areas under the receiver operating characteristics curves for the associ

214 The area under the receiver-operating characteristic curve for prediction o

215 sk factors, and to calculate areas under the receiver-operating characteristic curves for the presenc

216 Receiver operating characteristic curve had an area unde

217 With a receiver operating characteristic curve, IDO activity ha

218 Analysis of receiver operating characteristic curves implicates aber

219 Receiver operating characteristic curve, Kaplan-Meier me

220 nce in some datasets was low (area under the receiver operating characteristic curve, < 0.7) for the

221 omarker of severe attention impairment (peak receiver operating characteristic curve measured by area

222 ariate random-effects and hierarchic summary receiver operating characteristic curve models.

223 ur prediction method shows an area under the Receiver Operating Characteristic curve of 0.85 for all

224 iagnostic values with maximum area under the receiver operating characteristic curve of 0.878 for CCA

225 nute advanced warning with an area under the receiver operating characteristic curve of 0.91.

226 elocity-time integral with an area under the receiver operating characteristic curve of 0.938 (0.785-

227 30 days after injury, with an area under the receiver operating characteristic curve of 0.939 in the

228 95% CI, 88-96), and a summary area under the receiver operating characteristic curve of 0.95 (95% CI,

229 NLST database demonstrated an area under the receiver operating characteristic curve of 0.963 (95% co

230 -validation Area Under Curve of 0.85 for the Receiver Operating Characteristic curve of our model.

231 The area under the receiver operating characteristic curve of SVC was signi

232 The area under the receiver operating characteristic curve of the Amsler gr

233 t disease, and the diagnostic area under the receiver operating characteristic curve of the associati

234 The area under the receiver operating characteristic curve of the King's Co

235 The area under the receiver operating characteristic curve of the ratio of

236 dentified in models that had areas under the receiver operating characteristic curves of 0.57 (95% CI

237 Pooled sensitivity, specificity, and summary receiver operating characteristic curves of each imaging

238 An analysis of the receiver operating characteristic curves of published da

239 M65 and M30 both had an area under the receiver operating characteristics curve of 0.84 to esti

240 tion cohort, the score had an area under the receiver-operating characteristic curve of 0.87 (p < 0.0

241 pendent predictor of outcome (area under the receiver-operating characteristic curve of the model = 0

242 Areas under the receiver-operating characteristic curves of the 2 differ

243 for obstructive CAD, with an area under the receiver-operating characteristics curve of 0.713 versus

244 ction models when assessed by area under the receiver operating characteristics curve or net reclassi

245 fixed- or random-effects pooling or summary receiver operating characteristic curve) or recommended

246 In receiver operating characteristic curves, percentage and

247 fy intraretinal and SRF using area under the receiver operating characteristics curves, precision, an

248 ood accuracy (cross-validated area under the receiver operating characteristic curve [principal + sec

249 shed AD from CN participants (area under the receiver operating characteristic curve range [95% CI],

250 Receiver operating characteristic curves rendered a CITP

251 sion-weighted imaging scores (area under the receiver operating characteristic curves, respectively,

252 iver operating characteristic and area under receiver operating characteristic curves revealed CCI to

253 Receiver operating characteristic curves revealed that l

254 Receiver operating characteristics curve revealed that r

255 ic risk differed by MS group by applying the receiver operating characteristic curve (ROC) cut point.

256 The receiver operating characteristic curve (ROC) was used t

257 en the biomarker panel and the state and the receiver operating characteristic curves (ROC curves) an

258 However, receiver operating characteristic curves (ROC) curve ana

259 nd Scotland by assessing the areas under the receiver operating characteristics curves (ROC-AUCs).

260 Area under the receiver operating characteristic curve, sensitivity, sp

261 erformance by calculating the area under the receiver operating characteristic curve, sensitivity, sp

262 with ILD compared with control subjects with receiver operating characteristic curves separating thes

263 Receiver operating characteristics curve showed aspartat

264 A summary receiver-operating-characteristics curve (SROC) was cons

265 Receiver operating characteristic curves summarizing dia

266 NWIS-model (0.85) had a lower area under the receiver-operating characteristics curve than the Early

267 s limited, with an AUC score (area under the receiver operating characteristic curve) that reaches 0.

268 d to accurately predict (>80% area under the receiver operating characteristic curve) the clinical en

269 The area under the receiver operating characteristic curve to distinguish N

270 nd sleep quality; and predictive validity by receiver operating characteristic curve to predict the t

271 Lastly, we used receiver operating characteristic curves to show that se

272 Area under the receiver operating characteristics curves to detect ball

273 developed de novo HCC with an area under the receiver operating characteristic curve value higher tha

274 ning diet vs controls with an area under the receiver operating characteristic curve value of 0.95 (9

275 liac disease on a GFD with an area under the receiver operating characteristic curve value of 0.96 (9

276 more data becomes available, area under the receiver operating characteristic curve values increase

277 Area-under-the-receiver-operating characteristic curve values for class

278 Area under the receiver operating characteristic curve was 0.85 (95% co

279 Area under the receiver operating characteristic curve was 0.88 (95% co

280 Area under the multivariate model receiver operating characteristic curve was 0.881.

281 st diagnostic properties, the area under the Receiver Operating Characteristic curve was 0.89 (95 % C

282 on-specific enolase (NSE; the area under the receiver operating characteristic curve was 0.91 for tau

283 ues were 93% and 90%, and the area under the receiver operating characteristic curve was 0.937.

284 Area under the receiver operating characteristic curve was 93.2% (95% C

285 7 to June 2013, out-of-sample area under the receiver operating characteristic curve was approximatel

286 (0.5%) and 91.6% (0.1%), the area under the receiver operating characteristic curve was between 0.94

287 The area under the curve of the receiver-operating characteristic curve was 0.65 (95% CI

288 position of the National Early Warning Score receiver-operating characteristic curve was above and to

289 The accuracy evaluated by the receiver-operating characteristic curve was higher for C

290 A summary receiver-operating-characteristic curve was constructed,

291 s correlation coefficient and area under the receiver operating characteristic curve were 0.581 and 0

292 The areas under the receiver operating characteristic curve were analyzed an

293 Receiver operating characteristic curves were employed t

294 Receiver operating characteristic curves were used to de

295 Receiver operating characteristic curves were used to de

296 Moreover, receiver-operating-characteristic curves were used to te

297 as assessed by sensitivity, specificity, and receiver operating characteristic curves, while associat

298 gression models to calculate odds ratios and receiver operating characteristic curves with area under

299 ed fluid responsiveness with areas under the receiver operating characteristic curves (with 95% CIs)

300 estimate odds ratios and the area under the receiver operating characteristic curve, with 95% CIs, o