ライフサイエンスコーパス: AUC

1 AUC values from AI-assisted analysis were significantly

2 AUC's for NFS and FIB-4 index were 0.86 and 0.85.

3 AUCs for discriminating advanced fibrosis (CPA >10.3%) w

4 ine (18 mg/mL) e-liquid (C(max), p = 0.0001; AUC(0-120 min), p = 0.0026).

5 s outperformed comparable classifiers (>0.10 AUC) and our interpretation methods were validated using

6 = 0.026) and optimal cut-off value was 1039 (AUC: 0.801; P = 0.002).

7 Patients with day 2 AUC <=515 experienced the best global outcomes (no TF an

8 d 600, 0 and 800, and 100 and 800 sec/mm(2); AUC, 0.73-0.76) provided results similar to those seen w

9 , and American Indian/Native Alaskan (n=223; AUC, 0.938).

10 8; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), respectively (P < .001 for AUC).

11 (n=651; AUC, 0.937), Hispanic/Latino (n=331; AUC, 0.937), and American Indian/Native Alaskan (n=223;

12 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), re

13 In the temporal validation cohort (n = 473), AUCs were 0.86 and 0.88.

14 under the curve [AUC], 0.931), Asian (n=557; AUC, 0.961), black/African American (n=651; AUC, 0.937),

15 6 (95% confidence interval [CI]: 1.48, 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.

16 AUC, 0.961), black/African American (n=651; AUC, 0.937), Hispanic/Latino (n=331; AUC, 0.937), and Am

17 In the external validation cohort (n = 722), AUCs were 0.86 and 0.88 but required calibration (Hosmer

18 nosis), the proposed approach predicted 0.79 AUC (0.66-0.91) with 0.76/0.71.

19 nd combustible cigarettes (C(max), p = 0.79; AUC(0-120 min), p = 0.13).

20 interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA sta

21 ielded AUC(ImageFusion) = 0.85 [0.82, 0.88], AUC(FeatureFusion) = 0.87 [0.84, 0.89], and AUC(Classifi

22 A maximum CVR < 0.9 (AUC, 0.72; 95% CI, 0.67-0.85) was associated with a low

23 ombined metabolites) had very good accuracy (AUC, 0.84-0.89) in differentiating patients at risk of d

24 It achieved AUC of 0.730 (95% confidence interval [CI]: 0.605, 0.844

25 cant predictor of intermediate-to-high ADNC (AUC = 0.72), whereas mean (18)F-FDG uptake was not (AUC

26 0%) and cognitively unimpaired older adults (AUC=90.21-98.24% across cohorts), as well as other neuro

27 tia from amyloid beta-negative young adults (AUC=99.40%) and cognitively unimpaired older adults (AUC

28 an a model(1) that considers symptoms alone (AUC = 0.71; IQR: 0.63-0.79).

29 Model accuracy was similar when ALVEOLI (AUC, 0.94; 95% CI, 0.92-0.96) and FACTT (AUC, 0.94; 95%

30 Quinolinate achieved an AUC of 0.77 after 1 month of treatment, and pyridoxate a

31 rformed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814-0.851) and an AUC of 0.798 (9

32 nth of treatment, and pyridoxate achieved an AUC of 0.87 after successful treatment completion.

33 els based on RNFL en face images achieved an AUC of 0.88 for identifying eyes with GVFD and 0.82 for

34 , specificity of 86% (95% CI, 75-93%) and an AUC of 0.78, correctly classifying 83% of the validation

35 an AUC of 0.824 (95% CI 0.814-0.851) and an AUC of 0.798 (95% CI 0.789-0.818) in the validation coho

36 accuracy on 10-fold cross-validation and an AUC of 0.883 with 83% accuracy in a blind test dataset o

37 observed for IgA reactivity to Rv0134 and an AUC of 0.98 for IgA reactivity to both Rv0629c and Rv218

38 mproved the performance of the model from an AUC of 0.724 (95% CI 0.625, 0.823) to 0.810 (95% CI 0.72

39 In comparison, model 4 had an AUC of 0.65.

40 ns of disease extent and opacity type had an AUC of 0.69 (P < .0001).

41 These consensus guidelines recommend an AUC/MIC ratio of 400-600 mg*hour/L (assuming a broth mic

42 The clinical DD score revealed an AUC of 0.417 (SE = 0.07, p = 0.191).

43 TBF and ADC of tumoural regions revealed an AUC of 0.808 and accuracy of 72.7%.

44 The nomogram showed an AUC of 0.89, a sensitivity of 0.86, and a specificity of

45 tic accuracy of 88.1% in our dataset with an AUC at the ROC curve of 0.85, whereas the accuracy value

46 as a fair diagnostic ability for CRC with an AUC of 0.63 (95%CI = 0.55-0.71).

47 C of 0.66 (P < .001), and the AIMS65 with an AUC of 0.64 (P < .001).

48 f 0.87 (P = .004), the Rockall score with an AUC of 0.66 (P < .001), and the AIMS65 with an AUC of 0.

49 es discriminated between ADNC stages with an AUC of 0.79, 0.88, and 0.90 for mean (18)F-FDG uptake, m

50 st CT in outpatient versus inpatient with an AUC of 0.84 (P < .005), while radiologists' interpretati

51 oxate) identified treatment response with an AUC of 0.86.

52 ndpoint with an AUC of 0.90, the GBS with an AUC of 0.87 (P = .004), the Rockall score with an AUC of

53 ients who met the composite endpoint with an AUC of 0.90, the GBS with an AUC of 0.87 (P = .004), the

54 t, accuracy of DeepCOVID-XR was 83%, with an AUC of 0.90.

55 Chest CT yielded an AUC of 0.87 (95% CI: 0.84, 0.89) compared with RT-PCR an

56 , called the GlycoTransplantTest, yielded an AUC of 0.95 for association with graft loss at 3 months.

57 erning disease severity (ROC curve analysis: AUC = 0.746, P = 0.027) was superior to each indicator s

58 ere AUC/MIC by broth microdilution >=650 and AUC/MIC by Etest >=320.

59 classifier obtained a F1 score of 0.71, and AUC of 0.75.

60 tervals] of AUC(DCE) = 0.85 [0.82, 0.88] and AUC(T2w) = 0.78 [0.75, 0.81].

61 AUC(FeatureFusion) = 0.87 [0.84, 0.89], and AUC(ClassifierFusion) = 0.86 [0.83, 0.88].

62 s 92%, sensitivity 95%, specificity 98%, and AUC 0.92.

63 I 0.53-0.81) for the ovarian dose model, and AUC of 0.96 (0.94-0.97) and AP of 0.46 (0.34-0.61) for t

64 ROC and AUC risk score analysis suggested that MSA4A (AUC: 0.894

65 inson's disease or multiple systems atrophy (AUC=81.90%).

66 The corresponding average AUC obtained are 0.883, 0.876, 0.868 and 0.883, respecti

67 e FA, with a combined cross-validation-based AUC of 0.73.

68 here was no statistical significance between AUCs for the macular parameter and cpRNFL thickness meas

69 as far superior to any individual biomarker (AUC 0.90 [0.84-0.97]).

70 ar input features that presents a calculated AUC of 0.73.

71 (10 mg/kg, days 1 and 15) plus carboplatin (AUC 5, day 1) plus pegylated liposomal doxorubicin (30 m

72 features can be used to classify PDAC cfDNA (AUC = 0.88).

73 curve of receiver operating characteristics (AUC ROC) (0.978).

74 s accurately ranked the reference compounds (AUC-ROC >= 0.9).

75 4 hours to minimum inhibitory concentration (AUC/MIC).

76 revealed that effluent Sdc-1 concentrations (AUC = 0.82, P = 0.017) and serum Sdc-1 concentrations (A

77 , P = 0.017) and serum Sdc-1 concentrations (AUC = 0.84, P = 0.006) were associated with the developm

78 control (AUC = 0.94), and viral and control (AUC = 0.83), with slightly more modest discrimination be

79 iscrimination between bacterial and control (AUC = 0.94), and viral and control (AUC = 0.83), with sl

80 the receiver operating characteristic curve (AUC) analysis and diagnostic odds ratios against both re

81 the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPRC) c

82 the receiver operating characteristic curve (AUC) and differences between models were assessed using

83 the receiver operating characteristic curve (AUC) and label accuracy.

84 the receiver operating characteristic curve (AUC) and Pearson r, respectively.

85 the receiver operating characteristic curve (AUC) and the precision-recall curve (average precision [

86 the receiver operating characteristic curve (AUC) differences and optimal thresholds were determined

87 the receiver operating characteristic curve (AUC) for ADC, D(app), and K(app) to discriminate maligna

88 the receiver operating characteristic curve (AUC) for the diagnosis of enamel caries and dentin carie

89 the receiver operating characteristic curve (AUC) for the image-based model was 0.73 (95% confidence

90 the receiver operating characteristic curve (AUC) of 0.66 at diagnosis.

91 the receiver operating characteristic curve (AUC) values of 0.76 and 0.79 versus 0.66 and 0.62.

92 the receiver operating characteristic curve (AUC), a measure of binary discrimination performance, an

93 the receiver operating characteristic curve (AUC), calculated using internal validation techniques (b

94 the receiver operating characteristic curve (AUC), sensitivity, and specificity.

95 Area under the receiver operating curve (AUC) was calculated in symptomatic versus asymptomatic i

96 analysis, with the area under the ROC curve (AUC) as a figure of merit in the task of distinguishing

97 The area under the ROC curve (AUC) for the assessment of group 1 was 0.78 (95% CI: 0.7

98 s scored using the Area Under the ROC Curve (AUC) statistic.

99 dentified early-onset (area under the curve (AUC) 0.93, 95% confidence interval (CI) 0.87-1.00), but

100 omography (PET) scans (area under the curve (AUC) = 0.87-0.91 for different brain regions).

101 We also achieved area under the curve (AUC) at 0.9998 +/- 0.0002 (p < 0.0001, n = 224) with IHC

102 e [DPTRS] >=6.75), the area under the curve (AUC) C-peptide increased significantly from baseline to

103 C) curves analysis and Area Under the Curve (AUC) estimation were performed to assess the diagnostic

104 We ascertained the area under the curve (AUC) in an analysis of waitlist mortality to find optima

105 probing depth) had an area under the curve (AUC) of 0.694 (95% Confidence Interval: 0.612-0.776) and

106 significantly from an area under the curve (AUC) of 0.75 in larger LVs to 0.67 in smaller LVs (P = 0

107 or data resulted in an area under the curve (AUC) of 0.80 (interquartile range (IQR): 0.73-0.86) for

108 des, ANuPP achieved an area under the curve (AUC) of 0.831 with 77% accuracy on 10-fold cross-validat

109 erial taxa achieved an area under the curve (AUC) of 0.87 which was only marginally superior to a mod

110 lgorithms had a pooled area under the curve (AUC) of 0.88 (95% CI 0.84-0.91), and custom-built algori

111 aches the average best area under the curve (AUC) of 0.883 across the 37 circRNA datasets when compar

112 cy characterized by an Area Under the Curve (AUC) of 0.89.

113 f a chest tube with an area under the curve (AUC) of 0.93 (95% confidence interval, 0.89-0.98) compar

114 dation with an average area under the curve (AUC) of 0.95, for the 8-class problem.

115 sensitivity of 94.94%, area under the curve (AUC) of 0.9641, and specificity of 90% when tested on th

116 A remarkable area under the curve (AUC) of 1.0 was observed for IgA reactivity to Rv0134 an

117 The area under the curve (AUC) of multiparametric MRI signal to classify lesion ag

118 for comparison of the area under the curve (AUC) versus that from other combined MRI predictors of P

119 uced risk for hydrops [area under the curve (AUC), 0.93; 95% confidence interval (CI), 0.87-1.00].

120 identifying GVFD using area under the curve (AUC), sensitivity, and specificity.

121 ses were calculated as area under the curve (AUC).

122 by area under the concentration-time curve (AUC) and decision curve analyses, and the optimal model

123 area-under plasma concentration-time curve (AUC) of 5FU-SLN(4) was 3.6 fold high compared with 5-FU.

124 d cerebral ischemia (DCI) (area under curve (AUC) > 0.750), and had better results than clinical (Wor

125 classify AKs versus SCCs (area under curve (AUC) 0.814, precision score 0.833, recall 0.714) was con

126 aracteristic (ROC) curves; Area Under Curve (AUC) and accuracy were calculated and compared using Wil

127 ysis, the highest value of area under curve (AUC) for the GM/WM density ratio was found at the HC lev

128 tis-ml achieved an average area under curve (AUC) prediction performance of 0.81-0.89.

129 the receiver operating characteristic curve (AUCs) for determining hypermetabolic (18)F-FDG PET/CT fo

130 the receiver operating characteristic curve (AUCs) increased from 0.900 (95% CI 0.843-0.957) and 0.92

131 the receiver operating characteristic curve (AUCs) of 0.91, 0.88, and 0.88, respectively.

132 the receiver operating characteristic curve (AUCs) were 0.84 and 0.86 for predicting N1 or higher (vs

133 the receiver operating characteristic curve (AUCs) were higher for age-adjusted DL risk score than fo

134 ression analyses with areas under the curve (AUCs) as outputs were performed.

135 the receiver operating characteristic curve [AUC] 0.91) and progression to diabetes (AUC 0.92) based

136 the receiver-operating characteristic curve [AUC]) for incident lung cancer than CMS eligibility (PLC

137 the receiver operating characteristic curve [AUC], 0.97; 95% confidence interval [CI]: 0.94, 1.00) an

138 oplatin (area under the concentration curve [AUC] 4, day 1) plus gemcitabine (1000 mg/m(2), days 1 an

139 agnostic value for AR (area under the curve [AUC] 0.51, 95% confidence interval [CI] 0.50-0.56; P = .

140 ls with periodontitis (area under the curve [AUC] = 0.85 95% CI 0.78-0.92) and its moderate/severe fo

141 low (F (p) ) skewness (area under the curve [AUC] = 0.924) and combined D* standard deviation and F (

142 ofilament light chain (area under the curve [AUC] = 0.95).

143 efined AD from non-AD (area under the curve [AUC], 0.89 [95% CI, 0.81-0.97]) with significantly highe

144 panic white (n=44 524; area under the curve [AUC], 0.931), Asian (n=557; AUC, 0.961), black/African A

145 ntravenous carboplatin area under the curve [AUC]5 or AUC6 and 175 mg/m(2) intravenous paclitaxel eve

146 travenous carboplatin (area under the curve [AUC]5 or AUC6) and intravenous paclitaxel (175 mg/m(2) b

147 the receiver operator characteristic curves (AUC) and accuracy were determined.

148 he receiver operating characteristic curves (AUC) and partial AUC for the region of clinically meanin

149 he receiver operating characteristic curves (AUC).

150 ssifiers yielded areas under the ROC curves (AUCs) [95% confidence intervals] of AUC(DCE) = 0.85 [0.8

151 eiver operating characteristic (ROC) curves (AUCs) on the test set were 0.88 (95% confidence interval

152 at was trained on both VF and clinical data (AUC, 0.89-0.93) showed better diagnostic ability than a

153 omal fever and respiratory symptoms >6 days (AUC = 0.79), and PCT <0.25 mug/L (AUC = 0.81) improved d

154 isorders, including frontotemporal dementia (AUC=82.76-100% across cohorts), vascular dementia (AUC=9

155 .76-100% across cohorts), vascular dementia (AUC=92.13%), progressive supranuclear palsy or corticoba

156 rve [AUC] 0.91) and progression to diabetes (AUC 0.92) based on standard cross-validation (CV).

157 lume performed better than maximal diameter (AUC, 0.79 vs 0.53, respectively; P = .01).

158 There was modest discrimination (AUC = 0.73) between sepsis with organ dysfunction and in

159 ementia vs other neurodegenerative diseases (AUC, 0.96 [95% CI, 0.93-0.98]) was significantly higher

160 eing significantly better than chance (i.e., AUC = 0.50) (p < 0.01).

161 K1IP1 (AUC: 0.785, 0.806, 0.977), and EPHB2 (AUC: 0.794, 0.723, 0.620) at 0, 6, and 12 months follow-

162 l in the SJLIFE cohort produced an estimated AUC of 0.94 (95% CI 0.90-0.98) and AP of 0.68 (95% CI 0.

163 LI (AUC, 0.94; 95% CI, 0.92-0.96) and FACTT (AUC, 0.94; 95% CI, 0.92-0.95) were used as the validatio

164 rior to a model without microbiota features (AUC 0.85).

165 val (CI) 0.87-1.00), but not late-onset FGR (AUC 0.70, 95% CI 0.64-0.75).

166 1.25; AUC, 0.57), respectively (P < .001 for AUC).

167 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, spec

168 tent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, spec

169 CI 0.78-0.92) and its moderate/severe form (AUC = 0.86 95% CI 0.79-0.04) with sensitivity and specif

170 SNFL thickness has great AUC and correlation with VFMD in glaucomatous eyes.

171 Only WFNS and VASOGRADE had AUC > 0.700, with better performance than mFS (P < 0.05)

172 n predicting 1-year mortality remained high (AUC = 0.85).

173 ccordingly, in smaller LVs, PET had a higher AUC (0.77) than the SPECT AUC (0.67) (P < 0.0001), a phe

174 %CL = 15.4, 72.1) than for A:A homozygotes (%AUC difference = 6.5, 95%CL = -30.2, 43.2).

175 r (P = 0.003) efficacy for G:G homozygotes (%AUC difference = 43.7, 95%CL = 15.4, 72.1) than for A:A

176 requency hearing loss (frequencies >9000 Hz; AUC = 0.81) but weaker for clinically determined hearing

177 n of structure model-based features improved AUC from 0.611 and 0.520 to 0.655 and 0.566 for lysine a

178 ate by 0.18 (90% CI: 0.08 to 0.29 at 90%) in AUC and 0.30 (90% CI: 0.11 to 0.50) in specificity when

179 n had the highest fractional improvements in AUC at higher image resolutions.

180 n of anterior caries significantly increased AUC in all year 3 models with or without imputation (all

181 ificantly predicted SSI (<24.6 min infusion, AUC = 0.762).

182 e to reference subspecialist interpretation (AUC = 0.99).

183 ed D* standard deviation and F (p) kurtosis (AUC = 0.916) for prediction of objective and complete re

184 s >6 days (AUC = 0.79), and PCT <0.25 mug/L (AUC = 0.81) improved diagnostic performance (AUC = 0.90)

185 showed good diagnostic accuracy; the largest AUCs reached 0.875 for the LDF and 0.879 for global RNFL

186 11 inputs, in both the PLCO data set (CXR-LC AUC of 0.755 vs. PLCO(M2012) AUC of 0.751) and the NLST

187 /WM density ratio was found at the HC level (AUC = 0.907).

188 aker for clinically determined hearing loss (AUC = 0.60).

189 al and kurtosis derived parameters had LPOCV AUC in the range of 0.72 to 0.92 while the corresponding

190 AFF derived parameters did not improve LPOCV AUC.

191 mance was similar in larger and smaller LVs (AUC, 0.79 vs. 0.77, P = 0.49).

192 ata set (CXR-LC AUC of 0.755 vs. PLCO(M2012) AUC of 0.751) and the NLST data set (0.659 vs. 0.650).

193 0.93, respectively; P = .63) and T2 mapping (AUC, 0.97 vs 0.97, respectively; P > .99).

194 Maximum AUCs were achieved at image resolutions between 256 x 25

195 models ranged from 0.44 to 0.60 and the mean AUC ranged from 0.47 to 0.59.

196 curacy ranged from 0.62 to 0.75 and the mean AUC ranged from 0.64 to 0.81.

197 than using mean RNFL thickness measurements (AUC = 0.82 and 0.73, respectively).

198 asma P-tau181, plasma NfL, and MRI measures (AUC range, 0.50-0.81; P < .001) but not significantly di

199 CSF Abeta42:Abeta40 ratio, and MRI measures (AUC range, 0.67-0.90; P < .05), but its performance was

200 The S63 transcriptomic metric (AUC 0.80) outperformed clinical markers and plasma inter

201 dictor for complete response at 6-12 months (AUC = 0.857).

202 UC risk score analysis suggested that MSA4A (AUC: 0.894, 0.644, 0.720), PDZK1IP1 (AUC: 0.785, 0.806,

203 ncept, we demonstrate this by multidetection AUC experiments at variable speed and time profiles.

204 -tau181 and neurofilament light chain (NfL) (AUC range, 0.50-0.72; P < .05).

205 .72), whereas mean (18)F-FDG uptake was not (AUC = 0.66), although the difference between methods was

206 curves (AUCs) [95% confidence intervals] of AUC(DCE) = 0.85 [0.82, 0.88] and AUC(T2w) = 0.78 [0.75,

207 rify the quantitative and accurate nature of AUC for assessment of NDDSs, that is, also future nanome

208 The values of AUC (area under curve) for all five hazards using the be

209 oil had greater effects than tricaprylin on AUCs of plasma CCK (+40%, P < 0.01) and NT (+32%, P < 0.

210 e performance for predicting a poor outcome (AUC > 0.750), and were better than the radiological scor

211 ting characteristic curves (AUC) and partial AUC for the region of clinically meaningful specificity

212 probability of GON by human graders (partial AUC = 0.529 vs 0.411, respectively; P = .016).

213 The partial AUC for the M2M DL algorithm was significantly higher th

214 improved the ability of MRI to predict PAS (AUC, 0.94 vs 0.89; P = .009).

215 In the validation set of 167 patients, AUC was 0.846.

216 MSA4A (AUC: 0.894, 0.644, 0.720), PDZK1IP1 (AUC: 0.785, 0.806, 0.977), and EPHB2 (AUC: 0.794, 0.723,

217 The mean (+/-SD) 4-hour C-peptide AUC at week 52 differed significantly between the golimu

218 mixed-meal tolerance test (4-hour C-peptide AUC) at week 52.

219 a-3p predicted differences in MMTT C-peptide AUC/peak levels at the 12-month visit; the combination m

220 rences in area under the curve percentages (%AUC) were calculated to signify efficacy.

221 le ADC metric provided the best performance (AUC, 0.79; 95% CI: 0.70, 0.88), and a threshold using me

222 ynuclein exhibited a consistent performance (AUC=0.86) in separating clinical Parkinson's disease fro

223 AUC = 0.81) improved diagnostic performance (AUC = 0.90) (P = .05).

224 strated the best distinguishing performance (AUC of 0.79).

225 The diagnostic performances (AUCs) were 0.82 +/- 0.02 and 0.75 +/- 0.03 for slice-wis

226 d (CSF) P-tau217, CSF P-tau181, and tau-PET (AUC range, 0.90-0.99; P > .15).

227 terval, 0.89-0.98) compared with pleural pH (AUC 0.82; 95% confidence interval, 0.73-0.90).

228 ministration showed that the liver-to-plasma AUC ratios could be significantly improved, compared to

229 ident lung cancer than CMS eligibility (PLCO AUC, 0.755 vs. 0.634; P < 0.001).

230 bolomic model predicted PE better than PlGF (AUC [95% CI]: 0.868 [0.844-0.891] vs 0.604 [0.485-0.723]

231 1), and custom-built algorithms had a pooled AUC of 0.93 (95% CI 0.85-0.97).

232 on of invasiveness with discriminative power AUCs of 0.83 to 0.87 and predicted the class probabiliti

233 he nomogram also had a discriminative power (AUC, 0.90; sensitivity, 0.85; specificity, 0.81) in iden

234 model obtains significant predictive power (AUC = 0.841).

235 with random forest analysis shows profound (AUC = 0.92, p-value = 3.16E-8) MP-induced alterations of

236 etry, K-mean, and astigmatism (respectively, AUC = 0.861, 0.779, 0.748, 0.700, 0.670).

237 Results AUC for distinguishing fibrotic (CPA >4.8%) from nonfibr

238 n a model exclusively trained on VF results (AUC, 0.79-0.82; P < 0.001).

239 ans but slightly less than in Europeans (ROC AUC 0.84 v 0.87, p < 0.0001).

240 performer with an accuracy of 90% and a ROC-AUC of 0.96.

241 The area under the ROC (ROC-AUC) when using these seven predictors was 0.88.

242 Model accuracy was 86% with ROC-AUC of 0.96.

243 criminated abnormal vs normal tau-PET scans (AUC, 0.93 [95% CI, 0.91-0.96]) with significantly higher

244 levels (MnInc) and total cortisol secretion (AUC(G)).

245 OCT showed significantly higher sensitivity, AUC and Kappa values than radiography.

246 ol-specified biopsies (N = 1276) separately (AUC of 0.53, 95% CI 0.50-0.59, P = .44 and 0.55, 95% CI

247 formance than late percentage T1 shortening (AUC, 0.97 vs 0.90, respectively; P = .03) and extracellu

248 n, EasyCIE-SSI had sensitivity, specificity, AUC of 79%, 92%, 0.852 for the detection of SSI, respect

249 EasyCIE-SSI had a sensitivity, specificity, AUC of 94%, 88%, 0.912 for the detection of SSI, respect

250 , PET had a higher AUC (0.77) than the SPECT AUC (0.67) (P < 0.0001), a phenomenon driven by female p

251 t tumor status and estrogen receptor status (AUC = 0.999, 0.94 respectively) as accurately as the mea

252 sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, spec

253 sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, spec

254 supranuclear palsy or corticobasal syndrome (AUC=88.47%), and Parkinson's disease or multiple systems

255 tively; P = .046), and similar to native T1 (AUC, 0.97 vs 0.93, respectively; P = .63) and T2 mapping

256 rget-AUC group vs. 52.5% in the below-target AUC group, (P=0.013).

257 atients experienced TCS; 69.7% in the target-AUC group vs. 52.5% in the below-target AUC group, (P=0.

258 cantly different compared with CSF P-tau217 (AUC, 0.96; P = .22).

259 st to delivery, VOC analysis is a good test (AUC 0.84) for the prediction of preterm birth with a sen

260 of the LFA was highly similar to GM testing (AUC 0.892 versus 0.893, respectively).

261 The AUC and accuracy were significantly greater using the AN

262 The AUC for predicting late AMD development was similar for

263 The AUC of a model combining all selected predictors was 0.8

264 The AUC of our MRS was 0.724 and higher than risk scores cre

265 The AUC was 0.86 (95% CI = 0.85-0.88) for AI and 0.83 (95% C

266 In subgroup analyses, the AUC was 0.95 (95% CI: 0.94 to 0.97) among patients with

267 inal symptoms (inverse association), and the AUC increased to 0.81.

268 hours was 0.86 (95% CI = 0.64-0.97), and the AUC of quantitative NWU was 0.91 (95% CI = 0.78-0.98).

269 1.2% accuracy with 92.78% sensitivity at the AUC of 90.90% on circR2Disease benchmark dataset.

270 symptom duration of less than 48 hours, the AUC fell to 0.71 (95% CI: 0.62, 0.80; P < .001).

271 600, and 800 sec/mm(2)) did not improve the AUC (0.74; P = .28), and several combinations of two b v

272 ography and clinical variables increased the AUC of intubation and death to 0.88 (95% CI: 0.79, 0.96)

273 year in each oral insulin group, whereas the AUC glucose increased significantly in each placebo grou

274 The vast majority of the viral load-time AUC lies within 10 days of symptom onset.

275 pulmonary targeting and lung residence time (AUC(inf) 141 +/- 3.2 vs 12.4 +/- 4.2 ID/g*hrs for ICAM a

276 be detected, with WSI self- and cross-tissue AUCs ranging from 0.65-0.80.

277 fied as being fearful according to the TPDS (AUC .86).

278 outperforms the predictive ability of tPSA (AUC 0.71), used in clinical practice.

279 aking use of analytical ultracentrifugation (AUC).

280 rinary galactose/creatinine were unreliable (AUC < 0.70) after milk ingestion.

281 The cross-validated AUC estimation in RS-I was 0.92 (95% confidence interval

282 The 5-fold cross-validated AUC for glaucoma versus nonglaucoma from logistic regres

283 dmissions with out-of-sample cross-validated AUCs of 0.64 and 0.70 respectively.

284 ose seen with calculations of four b values (AUC, 0.75; P = .17-.87).

285 discrimination between bacterial and viral (AUC = 0.78).

286 ectively; P = .03) and extracellular volume (AUC, 0.97 vs 0.88, respectively; P = .046), and similar

287 zed separately as co-primary endpoints, were AUC/MIC by broth microdilution >=650 and AUC/MIC by Etes

288 mpared to using only clinical features where AUC increased by 5.7% and 13.0% for OS and RFS, respecti

289 C=0.98 and from multiple system atrophy with AUC=0.94.

290 assessment for gangrenous cholecystitis with AUC of its ROC as 0.92 (95% CI: 0.80-1.00, p = 0.001) wi

291 ating PDAC I-II patients from controls, with AUC = 0.992 (95% CI 0.983-1.000), SN = 0.963 (95% CI 0.9

292 t using 5hmC densities in genes perform with AUC of 0.92 (discovery dataset, n = 79) and 0.92-0.94 (t

293 on's disease from other proteinopathies with AUC=0.98 and from multiple system atrophy with AUC=0.94.

294 Most notably, the pipeline predicts TKR with AUC 0.943 +/- 0.057 (p < 0.05) for patients without OA.

295 to distinguish NPC cases from controls with AUCs of 0.992 (95% confidence interval [CI], 0.983 to 1.

296 native T1, Gd-Hyd, and MR elastography with AUCs of 0.90 (95% CI: 0.83, 0.98), 0.84 (95% CI: 0.74, 0

297 A combination of age >5 years (AUC = 0.77), prodromal fever and respiratory symptoms >6

298 ed SI ratio with T2-weighted entropy yielded AUC of 0.95 (95% CI: 0.91, 0.99) and did not differ comp

299 The multiparametric schemes yielded AUC(ImageFusion) = 0.85 [0.82, 0.88], AUC(FeatureFusion)

300 as performance status and albumin, yielding AUCs as high as 0.84-0.85 for the prediction of day 28 o