ライフサイエンスコーパス: ROC

1 ROC analyses between eight atracurium-sensitized patient

2 ROC analysis demonstrated case experience and trainee le

3 ROC analysis demonstrated high accuracy for tumor detect

4 ROC analysis demonstrated significantly higher area unde

5 ROC analysis for SULmax/liver improved test specificity

6 ROC analysis of relative CBF in the PCC enabled discrimi

7 ROC analysis of thalamic volumes of the patients with AV

8 ROC analysis revealed an area under the ROC curve of 0.8

9 ROC analysis showed a significant discriminatory accurac

10 ROC analysis was performed to determine the optimal rCBV

11 ROC analysis was used to evaluate the diagnostic value o

12 ROC analysis yielded an optimal cut-off value of 2.245 (

13 ROC analysis yielded diaschisis thresholds of 0.62 for T

14 ROC curve analyses demonstrated 60-100% sensitivity and

15 ROC curve analysis indicated that the optimal cutoff poi

16 ROC curve analysis of an independent, more heterogeneous

17 ROC curve showed that miR-125a-3p abundant level may pre

18 ROC curves displayed an AUC higher than 0.8 and simulati

19 ROC curves indicated that baseline IgE against peanut an

20 ROC curves showed poor diagnostic performance of median

21 ROC curves to determine the optimal viral load cutoff pr

22 ROC indicated that Ve < 0.24 gave the largest area under

23 ROC operates in various subcellular fractions including

24 ROC regression was used to evaluate the effect of covari

25 ROC-derived best SULmax cutoffs were 3.2 on early (area

26 tor of survival (hazard ratio=2.98; P<0.001; ROC area under the curve=0.71; P<0.001) but not LVAD-fre

27 e=0.71; P=0.026) and 12 months (Cox P=0.036; ROC area under the curve=0.62; P=0.122), but calibration

28 D-free survival (hazard ratio=1.41; P=0.097; ROC area under the curve=0.56; P=0.314).

29 ad marginal discrimination at 3 (Cox P=0.23; ROC area under the curve=0.71; P=0.026) and 12 months (C

30 tive and False Positive trade-off, through a ROC curve analysis.

31 pecificity of AFP were 62.9% and 93.3% at an ROC - derived optimum cut-off level of 18mAU/ml.

32 receiver-operating characteristic analysis (ROC).

33 th detectable salbutamol (p(corr) > 0.5) and ROC analysis was performed to measure the predictive pot

34 umus, and resistant organic C (POC, HOC, and ROC, respectively), clay content, cation exchange capaci

35 zed with conditional logistic regression and ROC analysis to investigate changes in interpretation.

36 Using pCR as the reference standard and ROC curve methodology, %TOITN AUC was 0.60 (95% CI, 0.39

37 Cut-off values were established by applying ROC curve analysis.

38 y identified markers were evaluated applying ROC curves resulting in individual marker AUC >90% both

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

40 At ROC-defined thresholds, APRI (>/=0.33), AAR (>/=0.93), F

41 th a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN.

42 ug-target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC

43 receiver operating characteristic curve (AUC ROC) of 0.768 (95% CI 0.735-0.801) with an average optim

44 The AUC ROC curve for the detection of IBI for the PCT assay was

45 of cell viability (AUC-ROC for ATP=0.78; AUC-ROC for cell count=0.88), the combination of HO-1 and ce

46 further improved the predictive ability (AUC-ROC=0.92).

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

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

49 tics within random forests results in an AUC-ROC of 0.818 (95% CI, 0.812-0.824).

50 receiver operating characteristic curve (AUC-ROC) value of 0.680 (95% confidence interval [CI], 0.669

51 nder the curve receiver operating curve (AUC-ROC) values [Corrected].

52 receiver-operator characteristic curve [AUC-ROC]=0.89) than traditional endpoints of cell viability

53 traditional endpoints of cell viability (AUC-ROC for ATP=0.78; AUC-ROC for cell count=0.88), the comb

54 e of the strain ratio (4.25) was obtained by ROC curve analysis, the sensitivity and specificity for

55 validated receiver operating characteristic (ROC) analyses were performed.

56 Receiver operating characteristic (ROC) analysis and continuation ratio logistic regression

57 Receiver operating characteristic (ROC) analysis demonstrated that this integrated EV doubl

58 NOVA) and receiver operating characteristic (ROC) analysis revealed that the peak height ratios were

59 ssed with receiver operating characteristic (ROC) analysis to generate an area under the ROC curve (A

60 Receiver operating characteristic (ROC) analysis was generated to set optimal thresholds; a

61 ified and receiver operating characteristic (ROC) analysis was performed to assess sensitivity and sp

62 Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic val

63 Receiver operating characteristic (ROC) analysis was performed to determine how well each m

64 Receiver operating characteristic (ROC) analysis was performed to identify optimal threshol

65 Receiver operating characteristic (ROC) analysis was used to determine the diagnostic accur

66 Receiver operating characteristic (ROC) analysis was used to evaluate the accuracy of these

67 variance, receiver operating characteristic (ROC) analysis, and exact tests.

68 ysis, and receiver operating characteristic (ROC) analysis.

69 ssed with receiver operating characteristic (ROC) analysis.

70 -qPCR and receiver operating characteristic (ROC) analysis.

71 ined with receiver operating characteristic (ROC) analysis.

72 odels and receiver operating characteristic (ROC) analysis.

73 ets using Receiver Operating Characteristic (ROC) and Precision Recall (PR) curves.

74 Receiver operating characteristic (ROC) and visual grading characteristic analyses were per

75 4, with a receiver operating characteristic (ROC) area under the curve (AUC) of 72%.

76 ide had a receiver-operating characteristic (ROC) area under the curve (AUC) of 82% for predicting SI

77 idation's receiver operating characteristic (ROC) area under the curve (AUC): 71%; 95% CI: 62%, 81%].

78 tory on a Receiver Operating Characteristic (ROC) chart, beginning with an over-motivated state with

79 under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved

80 under the receiver operating characteristic (ROC) curve (AUC), number of false discoveries and statis

81 ased on a Receiver operating characteristic (ROC) curve - derived optimum cut-off level of >140mAU/ml

82 under the receiver operating characteristic (ROC) curve 0.78 and 0.80, respectively).

83 Paired-receiver operating characteristic (ROC) curve analyses revealed no statistical differences

84 ted using Receiver-Operating Characteristic (ROC) curve analysis (AUC) and its 95 % Confidence Interv

85 included receiver operating characteristic (ROC) curve analysis and noninferiority analysis.

86 ated with receiver operating characteristic (ROC) curve analysis for each method, with stress MBF and

87 e PCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the N

88 BF) data, receiver operating characteristic (ROC) curve analysis of the posterior cingulate cortex (P

89 Receiver operating characteristic (ROC) curve analysis resulted in area under the ROC curve

90 Receiver operating characteristic (ROC) curve analysis showed excellent predictive accuracy

91 Receiver operating characteristic (ROC) curve analysis was carried out on diagnostic decisi

92 Receiver operating characteristic (ROC) curve analysis was performed to define cut-off valu

93 d through receiver operating characteristic (ROC) curve analysis.

94 based on receiver operating characteristic (ROC) curve analysis.

95 ng by the receiver operating characteristic (ROC) curve analysis.

96 ation and receiver operating characteristic (ROC) curve analysis.

97 under the receiver operating characteristic (ROC) curve for case-control discrimination based on firs

98 a under a receiver-operating characteristic (ROC) curve for each analyte were used to determine assoc

99 C) of the receiver operating characteristic (ROC) curve for performance evaluation.

100 under the receiver operating characteristic (ROC) curve for the classifiers was 0.973 with a sensitiv

101 under the receiver operating characteristic (ROC) curve of 0.73 (95% confidence interval [CI], .69-.7

102 under the receiver operating characteristic (ROC) curve of 0.75 +/- 0.1, 0.80 +/- 0.1 and 0.89 +/- 0.

103 ploys the receiver operating characteristic (ROC) curve to minimize false discovery rate (FDR) and ca

104 A receiver operating characteristic (ROC) curve was calculated, showing the sensitivity and s

105 Receiver operating characteristic (ROC) curve was drawn to determine a cut-off ADC value fo

106 nerated a receiver operating characteristic (ROC) curve, and calculated area under the curve (AUC) an

107 under the receiver operating characteristic (ROC) curve, relative to the other five methods.

108 es with a receiver operating characteristic (ROC) curve.

109 ulated by receiver operating characteristic (ROC) curve.

110 under the receiver operating characteristic (ROC) curve.

111 imized by receiver operating characteristic (ROC) curve.

112 rea under receiver operating characteristic (ROC) curves (AUC) and sensitivities at fixed specificiti

113 eas under receiver-operating characteristic (ROC) curves (AUCs) were used to evaluate their predictiv

114 Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calc

115 sessed by receiver operating characteristic (ROC) curves and survival analyses.

116 arison of receiver operating characteristic (ROC) curves demonstrated that HAS-BLED had the best disc

117 ation and receiver operating characteristic (ROC) curves produced.

118 We used receiver operating characteristic (ROC) curves to determine the ability of LB and FIB-4 to

119 We used receiver operating characteristic (ROC) curves to present balance in sensitivity and specif

120 Receiver operating characteristic (ROC) curves were analyzed by evaluating the area under t

121 Receiver operating characteristic (ROC) curves were constructed to assess the performance o

122 Receiver operating characteristic (ROC) curves were constructed to describe the ability of

123 Receiver operating characteristic (ROC) curves were constructed to determine optimal discri

124 Receiver operating characteristic (ROC) curves were constructed to determine the optimal th

125 Receiver operating characteristic (ROC) curves were constructed to estimate the ability of

126 Receiver operating characteristic (ROC) curves were created.

127 Receiver Operating Characteristic (ROC) curves were obtained.

128 of TMJOA, receiver operating characteristic (ROC) curves were plotted.

129 Receiver operating characteristic (ROC) curves, area under the curve (AUC), and thresholds

130 Receiver operating characteristic (ROC) curves, category-free net reclassification index (c

131 Using receiver operating characteristic (ROC) curves, we determined the optimal cut-off values fo

132 nted with receiver operating characteristic (ROC) curves.

133 (AUC) of receiver operating characteristic (ROC) curves.

134 he use of receiver operating characteristic (ROC) curves.

135 red using receiver operating characteristic (ROC) curves.

136 tion uses receiver operating characteristic (ROC) curves.

137 ned using receiver operating characteristic (ROC) curves.

138 curve of receiver operating characteristic (ROC) in predicting stroke was VI value of LASEC (p < 0.0

139 ysis by a receiver operating characteristic (ROC) plot gave an area under the curve of 0.96, 80-83% c

140 using the receiver operating characteristic (ROC).

141 Receiver-operating-characteristic (ROC) analysis was performed to determine optimal cut-off

142 including receiver-operating-characteristic (ROC) comparison of early and delayed imaging sessions, w

143 red using receiver-operating-characteristic (ROC) curve analysis and Kaplan-Meier survival curves.

144 sessed by receiver-operating-characteristic (ROC) curve analysis.

145 n that of receiver-operating-characteristic (ROC) curves, confirming similar findings in other class-

146 rformed a receiver-operating-characteristic (ROC) study using PEM images reconstructed with different

147 aluated by Receiver-operator characteristic (ROC) analyses, and DIPP species emerged as potential IC

148 Receiver operator characteristic (ROC) analysis determined that a 45% catheter to vein rat

149 The receiver operator characteristic (ROC) curve analysis demonstrated that the CI candidacy e

150 Receiver operator characteristic (ROC) curve analysis showed that IL17A can be used to dis

151 Summary receiver operator characteristic (ROC) curve demonstrated superior accuracy of direct MR a

152 parison of receiver-operator characteristic (ROC) curves and Decision Curve Analysis (DCA).

153 under the receiver operator characteristic (ROC) curves and multivariate generalized additive model

154 rithm with receiver operator characteristic (ROC) curves and the weighted kappa statistic.

155 quisition, receiver operator characteristic (ROC) curves were used to delineate predictive factors.

156 modeling, receiver-operator characteristic (ROC) curves, and calibration plots.

157 ak height receiver operating characteristic [ROC] area under the curve [AUC] = 0.9362; P < .0001) (ET

158 under the receiver operating characteristic [ROC] curve [AUROC] for wPRx was 0.73 versus 0.66 for PRx

159 (AUC) of receiver operating characteristics (ROC) >0.95 for all the three types of adenocarcinoma ana

160 By receiver operating characteristics (ROC) analysis, cFFR provided better diagnostic performan

161 for the receiver operating characteristics (ROC) analysis.

162 Receiver operating characteristics (ROC) and principal component analysis (PCA) revealed neu

163 A receiver-operating characteristics (ROC) curve combining both lipid levels predicted EAD wit

164 The receiver operating characteristics (ROC) curve revealed a mean area under the curve (AUC) of

165 Receiver operating characteristics (ROC) were used to calculate the assay's diagnostic perfo

166 Receiver operating characteristics (ROC) were used, and the maximal-accuracy threshold in pr

167 nder the receiver operative characteristics (ROC) curves were 1.0, which represents a highly sensitiv

168 l presentation by miR-122 (derivation cohort ROC-area under the curve [AUC] 0.97 [95% CI 0.95-0.98]),

169 es the GTPase domain [termed Ras-of-complex (ROC) domain in this family] of human LRRK2 on the same r

170 ifunctional retinoid oxidoreductive complex (ROC).

171 outcome (p = 0.009), while the corresponding ROC curve had AUC of 0.784 and 0.783.

172 Receiver operating characteristic curve (ROC) analysis was used to examine the sensitivity and sp

173 the receiver operating characteristic curve (ROC) cut point.

174 The receiver operating characteristic curve (ROC) was used to compare the area under the ROC curve (A

175 and receiver operator characteristic curve (ROC) were used to assess the associations of biomarkers

176 73 m(2) at 1 year [receiver operating curve (ROC curve), 0.78, 95% CI, 0.68-0.89].

177 der receiver operating characteristic curve [ROC], 0.52; 95% confidence interval, 0.51-0.53).

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

179 he receiver operating characteristic curves (ROC curves) analysis for evaluation of clinical diagnosi

180 r, receiver operating characteristic curves (ROC) curve analysis revealed a significant area under cu

181 e receiver operating characteristics curves (ROC-AUCs).

182 Receiver operator curves (ROC) were constructed at an independent image level, mac

183 n model, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC-AUC, 0.740) cohorts.

184 The area under the curve (AUC) for each ROC curve serves as a quantitative metric to optimize tw

185 er the curve [AUC] = 0.9362; P < .0001) (ETP ROC AUC = 0.9362; P < .0001).

186 Finally, ROC appears to be one of many GTPases phosphorylated in

187 rkers had an area under the curve of 0.8 for ROC analysis and a sensitivity and specificity of 0.7 an

188 We then used these lead candidates for ROC analysis and found multiple biomarkers with values a

189 rage Specificity (MAS), a scalar measure for ROC curve, of 0.97 (0.96).

190 The global ROC curve for the Offsides ADE candidates ranked with th

191 ndent of the concentration of the individual ROC components.

192 The visual and quantitative analysis of ROC and PR curves shows that meta-predictors outperform

193 sepsis from severe SIRS (0.742-0.917 AUC of ROC curves).

194 say offered an area under the curve (AUC) of ROC curve similar to that for CRP concentration for the

195 On the basis of ROC curves, the most discriminative cutoff value for MTV

196 These features of ROC ensure that the rate of RA biosynthesis in whole cel

197 osphorylation also promotes the formation of ROC dimers, although GTPase activity appears to be equiv

198 0 and DHRS3, suggesting reduced formation of ROC.

199 Thus, the bifunctional nature of ROC provides the RA-based signaling system with robustne

200 Phosphorylation of ROC enhances its rate of GTP hydrolysis [from kcat (cata

201 On ROC and PASI-based anchor analysis, MCIDs equated to mea

202 ary MRP-8/14 was detected for periodontitis (ROC = 0.86).

203 For comparison with current practice, ROC curves relying solely on percent weight loss were al

204 on sites within the Ras of complex proteins (ROC) GTPase domain as well as some previously described

205 ylaxis and cardiovascular/febrile reactions, ROC curve analysis revealed a reasonably high area under

206 Regression/ROC analyses for hospitalizations were performed using c

207 n P2 were 83% of P0, and SUX and rocuronium (ROC) together made up 86% of sales throughout the study.

208 med a clinical data-only model (validation's ROC AUC: 61%; 95% CI: 50%, 71%; P = 0.01).

209 in the Danish (ROC-AUC, 0.739) and Scottish (ROC-AUC, 0.740) cohorts.

210 Summary ROC curve demonstrated overall superiority of 3-T imagin

211 ctively; the area under the curve in summary ROC was 0.87 (95% CI: 0.84-0.90).

212 We show that ROC is composed of at least two subunits of NAD(+)-depen

213 The ROC analysis identified RBP4 as a sensitive identifier o

214 The ROC analysis produced an area under the curve of 0.87, i

215 The ROC approach is flexible and can inform location-specifi

216 The ROC curve analysis identified an optimal cutoff value of

217 The ROC curve calculation provided the following optimum dia

218 The ROC curve decided that the cutoff point of FENO was 37.8

219 The ROC curve for ROPtool's tortuosity assessment had an are

220 The ROC curve of hypoxanthine returned an AUC of 0.79 (p < 0

221 en both THGr(Ce) and THGr(Cb) were below the ROC threshold, the combined diaschisis measures had a po

222 nt difference within the methods between the ROC curves (P > 0.4) for progression-free survival and o

223 In the forme fruste eyes, the ROC analysis showed that the AUC values of the mean K, t

224 inciple produce 50 positive instances in the ROC curves.

225 Importantly, disruption of the ROC-generated circuit by a knockdown of DHRS3 results in

226 Based on the ROC analysis, there were no satisfactory cut-off values

227 city based on optimal operating point on the ROC curve (7.0 ng/mL) were both 93%.

228 The AUC statistic quantified the ROC curve's capacity to classify participants likely to

229 efits, with a 17% increase in area under the ROC (AUROC) above the mean AUROC.

230 racy than CAP in terms of the area under the ROC curve (0.99 vs 0.77, respectively; P = .0334).

231 from healthy controls (global area under the ROC curve (AUC) 0.90 [95% CI 0.85-0.95]), latent tubercu

232 (ROC) analysis to generate an area under the ROC curve (AUC) and sensitivity and specificity distribu

233 ility, and approximately 0.89 area under the ROC curve (AUC) for disorder prediction.

234 The highest area under the ROC curve (AUC) for steatosis of PNPLA3 rs738409 genotyp

235 The area under the ROC curve (AUC) in the differentiation of stage F2 fibro

236 (ROC) was used to compare the area under the ROC curve (AUC) of each index.

237 FM framework, we compared the area under the ROC curve (AUC) of models developed with TRL-FM and othe

238 The area under the ROC curve (AUC) was highest for the 100%-count, 60-min i

239 aracteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifi

240 mance was evaluated using the area under the ROC curve (AUC), the Enrichment Factor (EF) and Hit Rate

241 ach to train it by maximizing area under the ROC curve (AUC), which is an unbiased measure for class-

242 e T1 and T2 yielded the best areas under the ROC curve (AUCs) of 0.975 and 0.979, respectively, for d

243 e combined score exhibited an area under the ROC curve (AUROC) of 0.81 for discriminating future deme

244 osis grade 0-1 vs 2-3 with an area under the ROC curve (AUROC) of 0.95 (95% CI, 0.91-0.98), and grade

245 ing best as determined by the area under the ROC curve (Mean AUC = 0.722).

246 ivity, 80%; specificity, 91%; area under the ROC curve [AUC] = 0.937; P = .0001), areas of lowest sig

247 The accuracies of ECVDEP (area under the ROC curve [AUC], 0.85) and normalized ECVDEP (AUC, 0.86)

248 no significant difference in area under the ROC curve between detector-reconstruction combinations a

249 The model yielded an area under the ROC curve exceeding 0.81 in an independent testing set,

250 DNI demonstrated the highest area under the ROC curve for diagnosis of VUR (0.620, 95% CI 0.542-0.69

251 The test set area under the ROC curve for predicting EGFR mutation status was 0.89.

252 without IFI (P = 0.618); the area under the ROC curve for predicting IFIs was 0.56 (poor test).

253 nstrated significantly higher area under the ROC curve for protocol B (P < .0022), with interreader a

254 e without IA (P = 0.098); the area under the ROC curve for the diagnosis of IA was 0.77 (fair test, i

255 Results The average area under the ROC curve for the groups increased with experience (0.94

256 The area under the ROC curve for the model and validation subsets of the di

257 r detection, with the highest area under the ROC curve obtained for LWF (0.97 in the peripheral zone

258 Furthermore, the area under the ROC curve obtained was 0.93, demonstrating that the prob

259 C) curve analysis resulted in area under the ROC curve of 0.676 (95% confidence interval: 0.58, 0.77)

260 and atypical bacteria with an area under the ROC curve of 0.79 (95% CI, .75-.82).

261 ROC analysis revealed an area under the ROC curve of 0.893 for FAcontra and of 0.815 for FAint,

262 tortuosity assessment had an area under the ROC curve of 0.917.

263 te urinary tract obstruction (area under the ROC curve ranged from 0.50 to 0.62).

264 The area under the ROC curve using these significant VOCs to discriminate e

265 racy was 64% (16/25), and the area under the ROC curve was 0.601 (95% confidence interval [95% CI], 0

266 The area under the ROC curve was 0.91 (0.80 with 4-fold cross-validation, P

267 (23/25) (P < 0.001), and the area under the ROC curve was 0.929 (95% CI, 0.740-0.990).

268 (25/25) (P < 0.001), and the area under the ROC curve was 1 (95% CI, 0.863-1).

269 The area under the ROC curve was calculated for the ROPtool, and sensitivit

270 Area under the ROC curve was significantly higher (P = .002) for ADC (0

271 Area under the ROC curve with stress MBF and MPR as the outcome measure

272 rimination of dCON (P < .001; area under the ROC curve, 0.66).

273 morphologic assessment alone (area under the ROC curve, 0.767 vs 0.487 [P = .00005] and 0.802 vs 0.48

274 om immunocompetent patients (areas under the ROC curve, 0.91 and 0.97).

275 n the one for RV volume (mean area under the ROC curve, 0.96 +/- 0.02 vs 0.88 +/- 0.04; P = .009).

276 f intra-abdominal infection (areas under the ROC curve: 0.775 vs 0.689, respectively, P = 0.03).

277 he fourth postoperative day (areas under the ROC curve: 0.783 vs 0.671, P = 0.0002).

278 better predictive abilities [area under the ROC curves (AUCs) of 0.67 and 0.69] than did weight-for-

279 The area under the ROC curves for OD/LREs was 0.648 and 0.742 for LB and FI

280 LS mimics (difference between area under the ROC curves: p=0.0001 and p=0.0005; respectively).

281 The area under the ROC for using small-window entropy imaging to classify t

282 e image level, we achieved an area under the ROC of 92.78% with an accuracy of 87.63%.

283 macula level, we achieved an area under the ROC of 93.83% with an accuracy of 88.98%.

284 patient level, we achieved an area under the ROC of 97.45% with an accuracy of 93.45%.

285 the prediction rate of 0.843 area under the ROC plot due to the change in magnitude of the electrost

286 Area under the ROC.

287 Survival curves with the ROC optimal cutoff for each method separated the same gr

288 icenter, cluster-randomized, clinical trial (ROC-PRIMED [Resuscitation Outcomes Consortium Prehospita

289 igher in AD compared to controls, area under ROC curve = 0.96) was identified as a fragment of pleiot

290 dicted subsequent grade 3-4 GVHD (area under ROC curve, 0.76).

291 velopment of peak grade 3-4 GVHD (area under ROC curve, 0.88).

292 hin 1 year after transplantation (area under ROC curve, 0.90).

293 The area under ROC from three-dimensional combination of PGI/II-HpAb-OP

294 For (2), we achieved an area under ROC of 0.814 in predicting significant MMSE decline: our

295 Pairwise comparisons of areas under ROC curves (AUCs) from the different grading systems wer

296 arker was more predictive than another using ROC curves, but multiple logistic regression suggested s

297 nalized logistic regression, evaluated using ROC analysis and validated in an independent cohort of c

298 ned cerebrocerebellar diaschisis ratios with ROC thresholds for both forebrain and hindbrain had high

299 An inhibitor of sPLA2-X (ROC-0929) that does not inhibit other mammalian sPLA2s,

300 ptimal models for months 1, 2, and 3 yielded ROC curves with AUCs of 0.68 (95% CI: 0.63, 0.74), 0.75