ライフサイエンスコーパス: area under the curve

1 cation scores during the 2013 pollen season (area under the curve).

2 Performance was evaluated using the area under the curve.

3 nths over 2 to 4 years, and expressed as the area under the curve.

4 eceiver operating characteristic curves with area under the curve.

5 ion of cognitive impairment using age alone (area under the curve 0.68, 95% CI 0.60-0.76) significant

6 imilar in the 318-patient validation cohort (area under the curve 0.68, Hosmer-Lemeshow test P=0.41).

7 In this patient subset, LA strain (area under the curve 0.73) and LA volume (area under the

8 aspartate aminotransferase (AST) had highest area under the curve 0.761 (95% CI: 0.625-0.785).

9 EAL equation, improving the C statistic from area under the curve 0.83 (for REVEAL risk score, 95% CI

10 n (area under the curve 0.73) and LA volume (area under the curve 0.83) showed good predictive value

11 k of invasion from others with a lower risk (area under the curve 0.94, 95% CI 0.92-0.97).

12 Compound II showed an enhanced area under the curve (0-last) and decreased plasma clear

13 wer error (p < 0.001) and higher (p < 0.001) area under the curve (0.87; 95% CI, 0.85-0.90) for PaO2/

14 ng partitioning explained PAH blood content (area under the curve = 0.47 log fugacity + 0.34, r(2) =

15 curacy for predicting the treatment outcome (area under the curve = 0.72).

16 mortality (odds ratio, 4.3; 95% CI, 2.2-8.2; area under the curve = 0.83).

17 th shorter TTD (<4.5 years before diagnosis: area under the curve = 0.83-0.89; sensitivity = 72.7%-94

18 SCC and NoKT-SCC and predicted SCC relapses (area under the curve = 0.837; P < 0.05).

19 ved for 12-hour values of lactate clearance (area under the curve = 0.839; 95% CI, 0.751-0.927) with

20 emonstrated high classification performance (area under the curve = 0.85 [95% confidence interval {CI

21 a significant discriminative power (learning area under the curve = 0.87; test area under the curve =

22 he ESDI had acceptable performance features (area under the curve = 0.95, sensitivity 87% (95% confid

23 (learning area under the curve = 0.87; test area under the curve = 0.96).

24 edicted with the sodium prediction equation (area under the curve =0.95, 95% confidence interval 0.89

25 while it was lower for CS 2DST than 2DTagg (area under the curve, 0.61 versus 0.75; P<0.001).

26 76) and the GBT + omalizumab group (P < .01; area under the curve, 0.65).

27 ilarly good for regional LS 2DTagg and 2DST (area under the curve, 0.66 versus 0.59; P=0.08), while i

28 al [CI], 0.72-0.79) and all-cause mortality (area under the curve, 0.70; 95% CI, 0.67-0.73).

29 e in identifying genotype-positive patients (area under the curve, 0.71-0.75) and, in combination wit

30 d end point of RVAD or death within 14 days (area under the curve, 0.73).

31 superior ( P = .002) to the Clinical Model (area under the curve, 0.73; 95% CI, 0.66 to 0.80).

32 nguished noninflamed from inflamed biopsies (area under the curve, 0.74 and 0.70, respectively).

33 cation of epicardial BV <1.50 mV was 3.9 mV (area under the curve, 0.75; sensitivity, 60%; specificit

34 xacerbation in both the GBT alone (P < .001; area under the curve, 0.76) and the GBT + omalizumab gro

35 mplications also demonstrated excellent fit (area under the curve, 0.76).

36 cellent discrimination for 30-day mortality (area under the curve, 0.77 at 12 hr up to 0.93 at 5-10 d

37 = .03), and SAP pattern standard deviation (area under the curve, 0.77; 95% CI, 0.66-0.87; P = .01).

38 ated good performance for both RVAD implant (area under the curve, 0.78) and the combined end point o

39 1; 95% CI, 0.72-0.90), SAP mean sensitivity (area under the curve, 0.80; 95% CI, 0.69-0.88; P = .03),

40 l contraction fraction ratio had the highest area under the curve, 0.80; 95% confidence intervals, 0.

41 justment model has excellent discrimination (area under the curve, 0.81) and calibration.

42 hich was larger than for SAP mean deviation (area under the curve, 0.81; 95% CI, 0.72-0.90), SAP mean

43 ic accuracy for vascular cognitive disorder (area under the curve, 0.82 [95% confidence interval: 0.7

44 The final model (area under the curve, 0.82; 95% confidence interval, 0.7

45 ver operating characteristic curve analysis (area under the curve, 0.84 [95% CI, 0.73-0.95]; P < .001

46 ments from histopathology-negative segments (area under the curve, 0.84; P < 0.001), which gave 67% s

47 abnormal epicardial electrogram was 3.7 mV (area under the curve, 0.88; sensitivity, 100%; specifici

48 ), with 91% sensitivity and 82% specificity (area under the curve, 0.90; 95% confidence interval, 0.7

49 9%; specificity, 85.2%-92.6% vs 92.6%-96.3%; area under the curve, 0.92-0.96 vs 0.95, respectively; a

50 /CT assessment performed better than CE-MRI (area under the curve, 0.932 vs. 0.773).

51 minative performance than did the CVR model (area under the curve, 0.933 vs 0.843; P = .018).

52 is revealed a high accuracy of calprotectin (area under the curve, 0.94) in the differentiation of in

53 wed the best performance to discriminate CA (area under the curve, 0.95; 95% confidence intervals, 0.

54 d an excellent performance of PAAT (P<0.001; area under the curve, 0.98; 95% confidence interval, 0.9

55 as percentage of predicted than as z-score (area under the curve: 0.714-0.760 vs. 0.649-0.708, respe

56 r 1-year mortalities) and validation sample (area under the curve: 0.73 and 0.71, respectively).

57 ood discrimination in the derivation sample (area under the curve: 0.76 for 28-day and 0.72 for 1-yea

58 the score showed a good prediction of MACE (area under the curve: 0.76).

59 ificant hepatic fibrosis (stage F2-F4) (mean area under the curve: 0.93 [95% CI: 0.85, 0.97] vs 0.91

60 acy (sensitivity: 90.9%, specificity: 93.2%, area under the curve: 0.985) and improved diagnosis of e

61 curve for predicting adverse LV remodeling (area under the curve: 0.99, 0.94, and 0.95, respectively

62 k NGAL, but similar to the first creatinine (areas under the curve: 0.691, 0.653, and 0.686, respecti

63 ee survival (hazard ratio=1.41; P=0.097; ROC area under the curve=0.56; P=0.314).

64 71; P=0.026) and 12 months (Cox P=0.036; ROC area under the curve=0.62; P=0.122), but calibration was

65 arkers of acute IS (neuron-specific enolase: area under the curve=0.69; interleukin 6: area under the

66 of survival (hazard ratio=2.98; P<0.001; ROC area under the curve=0.71; P<0.001) but not LVAD-free su

67 arginal discrimination at 3 (Cox P=0.23; ROC area under the curve=0.71; P=0.026) and 12 months (Cox P

68 ighest accuracy to predict durable segments (area under the curve=0.73).

69 hresholding remained significantly reliable (area under the curve=0.76, P<0.001) during mapping of IM

70 e=0.87), endothelial activation transcripts (area under the curve=0.80), macrophage transcripts (area

71 ar, and combined modalities (single modality area under the curve=0.80, P<0.001; combined modality ar

72 e: area under the curve=0.69; interleukin 6: area under the curve=0.82).

73 r the curve=0.80, P<0.001; combined modality area under the curve=0.84, P<0.001).

74 rve=0.86), and interferon-gamma transcripts (area under the curve=0.84; P<0.0001 for all comparisons)

75 der the curve=0.80), macrophage transcripts (area under the curve=0.86), and interferon-gamma transcr

76 m those without and included NK transcripts (area under the curve=0.87), endothelial activation trans

77 Gs that provided substantial discrimination (area under the curve=0.90-0.99) for current heavy alcoho

78 95% CI, 1.28% to 24.24%, P = .03); reactance area under the curve, 19.46% (95% CI, 7.56% to 31.36%, P

79 3 h for 2 days, followed by busulfan (target area under the curve 4000 mumol/L per min per day for 4

80 /m(2) of body surface area] and carboplatin [area under the curve 5]) or the same chemotherapy regime

81 e 1,000 mg/m(2) every 3 weeks or carboplatin area under the curve 6 plus paclitaxel 175 mg/m(2) every

82 e of disseminated intravascular coagulation (area under the curve, 72.9%; specificity, 71.2%; sensiti

83 r operating characteristic curve measured by area under the curve, 79.3%).

84 Chronic-phase viral load area under the curve (adjusted odds ratio, 3; 95% confid

85 The mean area under the curve and 95% confidence interval (CI) fo

86 s and Measures: Change in visual acuity (VA) area under the curve and change in central subfield thic

87 nial, neck, and chest) exposures achieved an area under the curve and concordance statistic of 0.70 a

88 Validation cohort area under the curve and concordance statistics ranged f

89 ted in phase 2 and 3 sofosbuvir studies, the area under the curve and maximum concentration for sofos

90 Exposure to durvalumab increased cediranib area under the curve and maximum plasma concentration on

91 We demonstrated model performance using area under the curve and the Hosmer-Lemeshow test in the

92 -observer ( >/=70%) reliability for latency, area-under-the-curve and peak-to-peak amplitude.

93 racy, receiver operating characteristics and area under the curve, and kappa score.

94 ransfer and we identify the precision-recall area-under-the-curve as the best metric for this purpose

95 AdjCmax and AdjTAC-area under the curve at dosing interval were both higher

96 has also a high correlation (R = 0.76) with area under the curve at dosing interval.

97 ws that in 3rd POD, PCT and CRP have similar area under the curve (AUC) (0.775 vs 0.772), both better

98 very short circulation time ( 0.44h), lower area under the curve (AUC) (33mugh/mL) and high clearanc

99 e basis of the maximum cumulative viral load area under the curve (AUC) (P = .054).

100 thin-subject variability was similar for the area under the curve (AUC) (range 12.11-15.81) and the c

101 ived from a cohort containing 4395 patients (Area Under the Curve (AUC) 0.743) and validated on an in

102 CA2-deficient tumors with 98.7% sensitivity (area under the curve (AUC) = 0.98).

103 sensus for 167 mutant/WT comparisons with an Area Under the Curve (AUC) above 0.8.

104 Area under the curve (AUC) analysis was used for compari

105 ernal HIV acquisition was assessed using the area under the curve (AUC) and Brier score.

106 g characteristic (ROC) curve, and calculated area under the curve (AUC) and diagnostic parameters at

107 ylomicron carotenoid and fat-soluble vitamin area under the curve (AUC) and maximum content in the pl

108 e to predict achieving the standards with an area under the curve (AUC) between 0.83 and 0.96 (P < 0.

109 outcomes were the pharmacokinetic parameters area under the curve (AUC) concentration at the end of t

110 e prediction of Alzheimer disease have given area under the curve (AUC) estimates of <80%.

111 xygen/RS at 36 weeks had the highest AOR and area under the curve (AUC) for all outcomes.

112 ing C statistics for continuous outcomes and area under the curve (AUC) for binary outcomes.

113 The area under the curve (AUC) for each ROC curve serves as

114 eiver operating characteristic curve and the area under the curve (AUC) metrics on manually curated d

115 The area under the curve (AUC) obtained with SPT was not sig

116 The best predictor had an average area under the curve (AUC) of 0.71 compared to existing

117 Compared with HBA1c with adjusted area under the curve (AUC) of 0.88 (95% confidence inter

118 OC curve analysis revealed a reasonably high area under the curve (AUC) of 0.91 for ApoA1.

119 lays an excellent final performance, with an area under the curve (AUC) of 0.92.

120 the 3 tau indices demonstrated accuracy with area under the curve (AUC) of 1.000, 0.916, and 1.000, r

121 The primary outcome measure was the area under the curve (AUC) of cumulative pain scores fro

122 Compared to NTM, LTM reduced area under the curve (AUC) of FPD lesion scores during d

123 ent within 10 years of disease onset with an area under the curve (AUC) of more than 0.85 in both the

124 s evaluated with respect to calibration, and area under the curve (AUC) of receiver operating charact

125 We used the area under the curve (AUC) of the receiver operating cha

126 The area under the curve (AUC) of the resulting probability

127 (ROC) curves were analyzed by evaluating the area under the curve (AUC) to detect the sensitivity and

128 er operating characteristics, the calculated area under the curve (AUC) to differentiate glaucoma was

129 T and BAT was tree-nut dependent and yielded area under the curve (AUC) values ranging from 0.75 to 0

130 Area under the curve (AUC) was 0.984 for DCEMRI+HB phase

131 haracteristic curve was constructed, and the area under the curve (AUC) was determined along with the

132 sis was generated to set optimal thresholds; area under the curve (AUC) was used to show the discrimi

133 Sensitivity, specificity, area under the curve (AUC), AHI, Epworth Sleepiness Scal

134 The sensitivity, specificity, area under the curve (AUC), and positive predictive valu

135 Area under the curve (AUC), describing the discriminator

136 nation was measured using the time-dependent area under the curve (AUC), predicting 5-year risk; inte

137 Based on area under the curve (AUC), the range of liver TCA level

138 f receiver operator curves and estimation of area under the curve (AUC).

139 The estimated areas under the curve (AUC) were 0.6622 for BV/TV alone,

140 of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, P<0.00

141 ion (ICP >/= 20 mm Hg) was highest for ONSD (area under the curve [AUC] 0.91, 95% CI 0.88-0.95).

142 The gene set had high predictive capacity (area under the curve [AUC] 0.967), which was superior to

143 esentation by miR-122 (derivation cohort ROC-area under the curve [AUC] 0.97 [95% CI 0.95-0.98]), HMG

144 ted better islet function for low-dose RCEM (area under the curve [AUC] 24 968) compared with low-dos

145 sitive identifier of ATTR V122I amyloidosis (area under the curve [AUC] = 0.78; 95% CI, 0.67-0.88).

146 inguished PSP patients from controls and PD (area under the curve [AUC] = 0.872 vs controls, AUC = 0.

147 eks, plus carboplatin (dose equivalent to an area under the curve [AUC] of 6) for six cycles or pacli

148 s busulfan exposure (expressed as cumulative area under the curve [AUC]) and associated busulfan AUC

149 , P<0.001 (receiver operating characteristic area under the curve [AUC], 0.74; 95% confidence interva

150 in-hospital mortality was highest for NEWS (area under the curve [AUC], 0.77; 95% confidence interva

151 aging was significantly associated with pCR (area under the curve [AUC], 0.85).

152 (P < 0.001 for all comparisons): miR-142-5p (area under the curve [AUC], 0.854), miR-155 (AUC, 0.876)

153 core in thrombectomy patients was rCBF <20% (area under the curve [AUC], 0.89; 95% CI, 0.84, 0.94), w

154 c curves determined the diagnostic accuracy (area under the curve, AUC).

155 tial PK/PD predictors included 0- to 24-hour area under the curve (AUC0-24), maximum concentration (C

156 Post-drug plasma 24(S)-HC area under the curve (AUC8-72) values, an indicator of n

157 e calculated with the use of the incremental area under the curve (AUCi) method, and serum lipids wer

158 ues were calculated by using the incremental area under the curve (AUCi) method.Adding carbohydrate t

159 xcursion (MAGE) and postprandial incremental area under the curve (AUCpp).

160 Serum insulin and plasma flow areas under the curve (AUCs) were similarly elevated by

161 Receiver operating characteristic curves and areas under the curve (AUCs) were used to assess model p

162 increased metacognitive performance (type-II area under the curve, AUROC2), but had no impact on perc

163 on with similar pharmacokinetics to Gd-DTPA (area under the curve between 0 and 30 minutes, 20.2 +/-

164 demonstrated excellent performance, with an area under the curve between 0.90 and 0.97 per group.

165 ver-operating-characteristic analysis showed area under the curves between 0.79 and 0.92, with the hi

166 RV maximum observed plasma concentration and area under the curve; DRV Ctrough levels were slightly l

167 Area under the curve for 0-120 min for glucose and insul

168 300 mg/dL [>16.6 mmol/L], and p=0.02 for the area under the curve for 180 mg/dL [10.0 mmol/L]), but a

169 everages.As primary outcome, the incremental area under the curve for bread-derived blood glucose (-3

170 e highest receiver operating characteristics-area under the curve for citrate accumulation was observ

171 Area under the curve for different levels of coronary ar

172 Cumulative exposure as the area under the curve for each risk factor was determined

173 analysis revealed that only M-value and the area under the curve for glucose were independently rela

174 mulative intracranial hypertension exposure: area under the curve for intracranial pressure above 20

175 we calculated both imputation error and the area under the curve for patients meeting criteria for a

176 d the receiver operating characteristic with area under the curve for predicting glucocorticoid dose

177 The area under the curve for SBWC from -15 min to 135 min wa

178 Pulmonary Critical Care Medicine Fellow's area under the curve for size and function was 0.83 (95%

179 Results were analyzed over time and using area under the curve for the intention-to-treat and comp

180 Area under the curve for the satiety gut hormone GLP-1 w

181 eek, GFAP demonstrated a diagnostic range of areas under the curve for detecting MMTBI of 0.73 (95% C

182 The highest areas under the curve for our models as revealed by the

183 No differences were detected in areas under the curve for PI-RADS version 2 versus 1.

184 a-CEHC areas under the concentration curves [area under the curve from 0 to 24 h (AUC0-24h): 27.7 +/-

185 ical models to predict cancer status with an area under the curve from 0.68 to 0.92 depending cancer

186 The area under the curve from high-resolution intracranial p

187 tration [Cmax] and 77% and 82% lower AUC0-t [area under the curve from time 0 to the last time measur

188 d (<3% error) with good predictive accuracy (area under the curve: graft survival, 0.69; patient surv

189 antly higher when combining both biomarkers (area under the curve > 0.85, P < 0.001) and independentl

190 abolism at 3 mo were observed in responders (area under the curve > 0.85, P < 0.04).

191 ntagonist for adults who receive carboplatin area under the curve >/= 4 mg/mL per minute or high-dose

192 is and found multiple biomarkers with values areas under the curve >0.70 including interleukin 15.

193 nd 15-year risk scores were highly accurate (areas under the curve: >0.90).

194 After breakfast, the incremental area under the curve (iAUC) for plasma glucose [mg/dL .

195 e was also a significantly lower incremental area under the curve (iAUC) for total peroxide oxidative

196 ndial response at 4 h and on the incremental area under the curve (iAUC) of plasma triglycerides.Fort

197 Incremental area under the curve (iAUC) over 0-3 h for glucose (min

198 valuated using the integrated time-dependent area under the curve (iAUC).

199 ood ratio was 4.3 and 0.24 respectively; the area under the curve in summary ROC was 0.87 (95% CI: 0.

200 aders of the short MR imaging protocol, with areas under the curve in the range of 0.74-0.81 (83.8% c

201 lower (P = .02) airway symptom scores, with area-under-the-curve increases of 9.1 (15.1) versus 34.9

202 ve for intracranial pressure above 20 mm Hg (area under the curve-intracranial hypertension) was calc

203 ated, and its accuracy to predict MDD, using area under the curve, logistic regression, and linear mi

204 Glucose area-under-the-curve measured for the DSS condition was

205 anges in PA pressures were evaluated with an area under the curve methodology to estimate the total s

206 ne the optimal viral load cutoff produced an area under the curve of <0.80 for all viruses, suggestin

207 The general-use patients had an area under the curve of -32.8 mm Hg-day at the 1-month t

208 With an area under the curve of 0.6 to 0.7, the discriminative a

209 il counts (receiver operating characteristic area under the curve of 0.64, p<0.0001), but with a high

210 , and 77.3% (score 5 to 9; p < 0.001) and an area under the curve of 0.73.

211 crimination for severe exacerbations with an area under the curve of 0.75 (95% confidence interval [C

212 ff value for MA of 65 mm or greater returned area under the curve of 0.750 (P < 0.001) predicting E-H

213 developed a prognostic model, achieving the area under the curve of 0.79.

214 opulation showed good discrimination with an area under the curve of 0.79.

215 nd cranial radiation therapy dose yielded an area under the curve of 0.81 (95% CI, 0.76 to 0.86), whi

216 Our final model, 2HELPS2B, had an area under the curve of 0.819 and average calibration er

217 el consisting of these three proteins had an area under the curve of 0.82 for the classification of O

218 predictive (receiver operator characteristic area under the curve of 0.83) of subsequent severe acute

219 The ROC analysis produced an area under the curve of 0.87, indicating very good discr

220 th high sensitivity and specificity, with an area under the curve of 0.87.

221 ontrol subjects and patients with IS with an area under the curve of 0.90 (sensitivity: 85.6%; specif

222 model combining these three features had an area under the curve of 0.91 (95% CI: 0.86, 0.96) and a

223 operating characteristic analysis showed an area under the curve of 0.916 (95% confidence interval 0

224 rves between 0.79 and 0.92, with the highest area under the curve of 0.92 (95% confidence interval [C

225 ctors for a poor outcome at 5 years, with an area under the curve of 0.92 indicating excellent predic

226 perating characteristic analysis revealed an area under the curve of 0.922.

227 arge and small MI on cine MR images, with an area under the curve of 0.93 and 0.92, respectively.

228 yroiditis, the best result was obtained with area under the curve of 0.934, accuracy of 83.8%, sensit

229 demonstrated good overall accuracy, with an area under the curve of 0.935.

230 imination for in-hospital mortality, with an area under the curve of 0.94 (95% CI, 0.92-0.95).

231 dren better than cashew-specific IgE with an area under the curve of 0.94 vs 0.78.

232 emonstrated 89.6%-93.4% accuracy and average area under the curve of 0.96 in differentiating patients

233 twork is a sensitive and specific predictor (area under the curve of 0.96, P < 0.0001) for epilepsy,

234 perating characteristic analysis revealed an area under the curve of 0.978.

235 00 mg/m(2); every 21 days) plus carboplatin (area under the curve of 6 by modified Calvert formula; e

236 5 mg/m(2) paclitaxel and carboplatin (target area under the curve of 6) given 21 days apart, followed

237 frailty based upon a validated index with an area under the curve of 79.3% for high frailty and 82.3%

238 Area under the curve of ALP in predicting graft loss fro

239 re was no significant difference between the area under the curve of cFFR in the low- and iso-osmolal

240 In comparison, the area under the curve of early diastolic mitral annular v

241 inversely related to the stomach volume and area under the curve of hormone concentrations (P < 0.05

242 Receiver operating characteristics-area under the curve of initial lactate concentration wa

243 sus 9.4, P = 0.001), and along this line the area under the curve of LSM for the diagnosis of F3-F4 f

244 Analyses of area under the curve of SOWS total scores showed signifi

245 -erythrocyte sedimentation rate-improved the area under the curve of symptoms by 0.16 (95% CI, 0.11-0

246 best marker-fecal calprotectin-improved the area under the curve of symptoms by 0.26 (95% CI, 0.21-0

247 d as mean+/-2 SE, and P values comparing the area under the curve of the general-use cohort with outc

248 It is important to note that the area under the curve of the plasma concentration of (-)-

249 The median area under the curve of the postoperative time 0- to 48-

250 The area under the curve of the receiver-operating character

251 We obtained area under the curves of 90% for SI and 77% for future h

252 TEI on the acute CMR scan had an area-under-the-curve of 0.87 (95% confidence interval of

253 (1.2-3.1) and 2.1 (1.1-3.7), with unadjusted areas under the curve of 0.618 and 0.627, respectively.

254 nd a diagnosis of chronic bronchitis yielded areas under the curve of 0.72 (95% confidence interval [

255 Areas under the curve of 0.76 and 0.69 were obtained at

256 vasion (n = 219), volume and diameter showed areas under the curve of 0.81 (95% CI: 0.70, 0.91; P = .

257 tion was similar for raw and ratio measures: areas under the curve of 0.93 for Ara h 2-specific IgE v

258 m other subtypes of RCC were 142 and 38 with areas under the curve of 0.937 and 0.895, respectively.

259 redict cardiovascular death within one year (Area Under the Curve, or AUC, of 0.730 vs. 0.704, p < 0.

260 om-made algorithm: motor threshold, latency, area-under-the-curve, peak-to-peak amplitude and duratio

261 slope (range 0.92-1.15) and discrimination (area under the curve range 0.83-0.87) were similar acros

262 or 2013-2016 showed good predictive ability (area under the curve range: 0.76-0.98).

263 tor curve analyses with permutation testing (areas under the curve ranging between 0.61 and 1.0).

264 The area under the curve results are presented as mean+/-2 S

265 features (receiver operating characteristics area under the curve [ROC AUC]: 0.86; 95% CI: 0.80 to 0.

266 nce problem, we novelly incorporate the AUC (area under the curve) score into the optimizing objectiv

267 biomarkers in clinical models increased the area under the curve (SEM) for predicting the renal outc

268 The measurement of ADCP activity by the area under the curve showed significantly higher activit

269 t a continuous measure of cortisol response (area under the curve) showed that high and low levels of

270 mpared with the whole-genome PRSs when using area under the curve statistics, logistic regression, an

271 The MRI-derived RV showed the largest area under the curve to predict mortality (0.72) or its

272 6MWD receiver operating characteristic curve-area under the curve to predict mortality was 0.71, 0.70

273 r=0.99) and correlated strongly with the log area under the curve troponin (r=0.80) and strongly with

274 Almost half of these (418/916) reached an area under the curve value >0.80.

275 model demonstrated an accuracy of 99% and an area under the curve value of 0.97.

276 The fractional DNL area under the curve value was measured using stable iso

277 tion voxels to total surface had the highest area under the curve values (0.918; 0.894 and 0.890, res

278 sin showed a negative correlation with IPGTT area under the curve values (P <0.05).

279 lood, with greater maximal concentration and area under the curve values compared with nonresponding

280 Area under the curve values for predicting iFR were >0.9

281 Mutual differences, Spearman correlations, area under the curve values from receiver-operating char

282 lculated receiver-operating characteristics' area under the curve values to evaluate diagnostic accur

283 Area-under-the-curve values for methylone and MDC were g

284 The area under the curve was 0.63 to predict progression at

285 The area under the curve was 0.71 (95% CI, 0.69-0.74) for th

286 The derivation model area under the curve was 0.73, and Hosmer-Lemeshow test

287 The pooled area under the curve was 0.94 for discrimination of HGG

288 Three months postoperatively, GLP-1 area under the curve was associated with early satiety (

289 Cumulative pain area under the curve was determined using the trapezoida

290 ion was a good predictor of CIN3+ cases; the area under the curve was higher for sites 5611 in the pr

291 For blood, the area under the curve was higher with lilotomab predosing

292 el-free pharmacokinetic measurement based on area under the curve was identified as a robust imaging

293 ion studies revealed that doxorubicin plasma area under the curve was significantly higher (1.7-fold)

294 Receiver operating characteristic area under the curve was used to determine diagnostic ac

295 The C-index (area under the curve) was 0.76 (95% confidence interval,

296 readers of the standard MR imaging protocol, areas under the curve were 0.71-0.77 (83.8% CI: 0.62, 0.

297 e, 14-42; P<0.01) and median creatine kinase areas under the curve were 22 000 and 38 000 IU.h in the

298 Results of logistic regression and areas under the curve were compared between PI-RADS vers

299 using difference in median survival time and area under the curve with time-dependent receiver operat

300 Areas under the curve with the protocols were compared o