ライフサイエンスコーパス: positive predictive value

1 urve analysis, sensitivity, specificity, and positive predictive value.

2 Both exhibit poor positive predictive value.

3 was associated with low specificity and low positive predictive value.

4 sion predicts aGCM with high specificity and positive predictive value.

5 ith performance variation in sensitivity and positive predictive value.

6 ms of net benefit rather than sensitivity or positive predictive value.

7 idated and demonstrated high specificity and positive predictive value.

8 ents, 53 had high probability of having high positive predictive value.

9 ould be identified with high specificity and positive-predictive value.

10 tive performance in terms of sensitivity and positive predictive values.

11 ve LN, with better sensitivity, and negative/positive predictive values.

12 eased negative predictive values and reduced positive predictive values.

13 recall curve (average precision [AP; average positive predictive value]).

14 recovery (sensitivity 0.86, specificity 0.6, positive predictive value 0.81, negative predictive valu

15 vity (0.71, 0.67), specificity (0.78, 0.88), positive predictive value (0.74, 0.75), and negative pre

16 e interval): 1.573 (0.973-2.541), P = 0.032; positive predictive value = 0.257, negative predictive v

17 .73, sensitivity = 0.67, specificity = 0.80, positive predictive value = 0.73 and negative predictive

18 (5.7% vs 5.8%; AOR, 0.88; P < .001), higher positive predictive value 1 (14.5% vs 11.9%; AOR, 1.26;

19 a lower abnormal interpretation rate, higher positive predictive value 1, and higher specificity.

20 ue (98% vs 92.4%), and both scores have poor positive predictive value (10.9%).

21 cumulative positive predictive value of 91% (positive predictive value =100% in patients with baselin

22 Modification codes (n = 1387; kappa = 0.28; positive predictive value = 22%; F1 = 0.34) showed limit

23 Despite the relatively low intraprostate positive predictive value (34.0%) with (18)F-fluciclovin

24 ified using "Martin" (n = 970; kappa = 0.43; positive predictive value = 34%; F1 = 0.48) and "Angus"

25 w specificity (51.3%, 95%CI: 48.8, 53.8) and positive predictive value (37.1%, 95%CI: 34.4, 39.9).

26 d for 63 patients who lived at least a year (positive predictive value, 45.2%).

27 28% correlating with the presence of subAR (positive predictive value: 47%-61%).

28 %; P < .001; negative predictive value, 88%; positive predictive value, 49%), and a significant diffe

29 %; P < .001; negative predictive value, 94%; positive predictive value, 55%) were observed.

30 e cohort of children (n = 515; kappa = 0.61; positive predictive value = 57%; F1 = 0.64).

31 RS improvement and improver status, with 70% positive predictive value, 60% negative predictive value

32 82.9% sensitivity, 91.7% specificity, 96.7% positive predictive value, 64.7% negative predictive val

33 (</=3 mm; n = 95), specificity(82% vs. 62%), positive predictive value(66% vs. 50%) and area under cu

34 io, 16.2; 95% confidence interval, 8.0-34.4; positive predictive value, 66%; negative predictive valu

35 ence standard cohort improved (kappa = 0.73; positive predictive value = 70%; F1 = 0.75).

36 the extended algorithm, albeit with a higher positive predictive value (76.6%; 95% CI: 72.8% to 80.1%

37 CCB showed better predictive value (positive predictive value 85%, negative predictive value

38 60%; specificity - 91.3%; accuracy - 76.7%; positive predictive value - 85.7%; and negative predicti

39 injury (sensitivity 100%, specificity 93.3%, positive predictive value 88.0%), and starting photother

40 cific (99.7%) and had a significantly higher positive predictive value (90.0%, PPV) than QFT (96.5% s

41 highly predictive of late endpoint success (positive predictive value 92.9%).

42 83.3% sensitivity, 65.7% specificity, 45.5% positive predictive value, 92.0% negative predictive val

43 pecificity (96.4% versus 87.5% [P = 0.034]), positive predictive value (93.6% versus 80.9 [P = 0.028]

44 confidence interval = 0.78-0.91, p < 0.0001, positive predictive value = 93%).

45 pneumonia on HRCT, the classifier showed 81% positive predictive value (95% CI 54-96) for underlying

46 A SALT score >=5 had a 25% positive predictive value (95% CI: 10%-47%) and a SALT s

47 ecificity, 94% [95% CI: 79%, 99%], 30 of 32; positive predictive value, 97% [95% CI: 90%, 99%], 68 of

48 st sensitivity (99.3%), specificity (91.0%), positive predictive value (98.6%), and negative predicti

49 V with 89%-sensitivity, 76%-specificity, 89%-positive predictive value and 76%-negative predictive va

50 12-lead ECG, CineECG at baseline had a 100% positive predictive value and 81% negative predictive va

51 We calculated sensitivity, specificity, positive predictive value and negative predictive value

52 s 15.5% (31/200) and 96.6% (170/176), with a positive predictive value and negative predictive value

53 stem to assess its sensitivity, specificity, positive predictive value and negative predictive value

54 The positive predictive value and sensitivity of our tool wa

55 (1) score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (

56 Its positive predictive value and specificity were 100%.

57 CFPyL depicted more lymph nodes and improved positive predictive value and specificity when added to

58 d 0.97, and 0.37 and 0.94, respectively, and positive predictive values and negative predictive value

59 To identify high-risk patients, positive predictive values and specificities for acute M

60 the antibody tests (quality, accuracy level, positive predictive value) and what those tests might in

61 th 75.0% sensitivity, 97.1% specificity, 75% positive predictive value, and 91.9% negative predictive

62 ity, specificity, negative predictive value, positive predictive value, and accuracy.

63 Sensitivity, specificity, positive predictive value, and negative predictive value

64 reening study were sensitivity, specificity, positive predictive value, and negative predictive value

65 we calculated the sensitivity, specificity, positive predictive value, and negative predictive value

66 Sensitivity, specificity, positive predictive value, and negative predictive value

67 d to calculate the sensitivity, specificity, positive predictive value, and negative predictive value

68 nfirmed aGCM had a specificity, sensitivity, positive predictive value, and negative predictive value

69 stem to assess its sensitivity, specificity, positive predictive value, and negative predictive value

70 on, the diagnostic sensitivity, specificity, positive predictive value, and negative predictive value

71 Sensitivity, specificity, positive predictive value, and negative predictive value

72 ive samples with a sensitivity, specificity, positive predictive value, and negative predictive value

73 Sensitivity, specificity, positive predictive value, and negative predictive value

74 mal, demonstrating sensitivity, specificity, positive predictive value, and negative predictive value

75 S criteria, showed sensitivity, specificity, positive predictive value, and negative predictive value

76 Sensitivity, specificity, positive predictive value, and negative predictive value

77 sured sensitivity, specificity, negative and positive predictive values, and negative and positive li

78 structed, and test sensitivity, specificity, positive predictive values, and negative predictive valu

79 , we also assessed sensitivity, specificity, positive predictive values, and negative predictive valu

80 Sensitivity, specificity, positive predictive values, and NPVs were calculated for

81 we introduce the notion of patient-specific positive predictive value, assigning confidence to indiv

82 Overall assay specificity/positive predictive values based on a 5% prevalence rate

83 tive predictive values (70% to 100%) but low positive predictive values (below 50%).

84 fferences in image contrast, sensitivity, or positive predictive values between the 2 (68)Ga-OPS202 p

85 n: (18)F-FDG PET/CT had good specificity and positive predictive value but low to moderate sensitivit

86 edictors (CADD, MetaSVM, Eigen), with higher positive predictive value, comparable negative predictiv

87 ediction approach had a significantly higher positive predictive value compared to minimum inhibitory

88 ociated ligands with 1.5-fold improvement in positive predictive value compared with existing tools a

89 plotted along corresponding percentiles, the positive predictive value curves for qPET and DeltaSUV(m

90 Positive predictive value for AF episodes was 39.9%.

91 No difference was evident in positive predictive value for biopsies performed (PPV(3)

92 6 years; range, 25-94 years), resulting in a positive predictive value for biopsy of 43% (181 true-po

93 ysial antibody in a second blood sample, the positive predictive value for CD is virtually 100%.

94 y within the first months post-onset, with a positive predictive value for clinically relapsing disea

95 The positive predictive value for detecting referable diseas

96 LR-5 criteria also improved specificity and positive predictive value for HCC (R1, two fewer false-p

97 T-GIT, T-SPOT.TB, and TST modestly increases positive predictive value for incident TB, but markedly

98 Positive predictive value for MR imaging recalls was 9.3

99 In this cohort, the positive predictive value for mutation carrier status in

100 The positive predictive value for new or progressive multipl

101 M (P < .001), resulting in a decrease in the positive predictive value for recall for suspicious micr

102 increased less rapidly, resulting in a lower positive predictive value for recall.

103 the ESC hs-cTnT 0/1 h algorithm had a higher positive predictive value for rule-in, whereas the exten

104 The positive predictive value for SIRE was 25% and the negat

105 specificity, negative predictive value, and positive predictive value for TRG1 versus TRG2-4.

106 At an RAI cutoff of >=37, the positive predictive values for 30- and 90-day readmissio

107 isk patients, hs-cTnI >=120 ng/l resulted in positive predictive values for acute MI of >=70%.

108 However, extremely low positive predictive values for all outcomes at <9 indica

109 of the algorithm yielded an average of 95.8% positive predictive values for both cases and control su

110 ncluded in EPIONCHO-IBM but not ONCHOSIM) on positive predictive values for different serological thr

111 0.86 to 0.92, specificity from 0.83 to 0.97, positive predictive value from 0.70 to 0.93, and negativ

112 r equal to 4 with U-Net lesions improved the positive predictive value from 48% (28 of 58) to 67% (24

113 ATB 6 months prior to sputum conversion with positive predictive value > 6% at 2% prevalence.

114 There was high concordance (>70%) and high positive predictive value (>90%) of ECR and clinical sta

115 However, low sensitivity and low positive predictive value have led critics to argue that

116 ng the false positive rate and improving the positive predictive value in breast imaging interpretati

117 PlusoptiX showed a reasonable level of positive predictive value in community setting and the d

118 erroni adjusted alpha = .0125), with highest positive predictive value in phase 1 (64.0%) and highest

119 specificity, negative predictive value, and positive predictive value in qualitative analysis for de

120 -score (the harmonic mean of sensitivity and positive predictive value) in the training set was appli

121 b for tuberculosis infection is 93%, and the positive predictive value is 79%.

122 Considering its positive predictive value, it might allow to make a cons

123 iagnostic tool; however, the sensitivity and positive predictive value may be lower than previously r

124 l-time PCR is highly sensitive yet has a low positive predictive value, necessitating confirmatory te

125 Specificity, sensitivity, positive predictive value, negative predictive value and

126 the classifier for sensitivity, specificity, positive predictive value, negative predictive value, an

127 The values of sensitivity, positive predictive value, negative predictive value, an

128 nstrated excellent sensitivity, specificity, positive predictive value, negative predictive value, an

129 curves (Az value), specificity, sensitivity, positive predictive value, negative predictive value, an

130 iagnostic metrics (sensitivity, specificity, positive predictive value, negative predictive value, an

131 Sensitivity, specificity, positive predictive value, negative predictive value, an

132 come measures were sensitivity, specificity, positive predictive value, negative predictive value, po

133 of 82%, specificity of 93%, and negative and positive predictive values (NPV and PPV) of 82% and 93%,

134 , a negative predictive value of 0.91, and a positive predictive value of 0.79 in comparison to obser

135 81 observed non-cases), with a corresponding positive predictive value of 0.84% and a negative predic

136 d cortex, had the best sensitivity of 78.0%, positive predictive value of 100% and an accuracy of 78.

137 DP-43 pathology in extramotor brain regions (positive predictive value of 100%).

138 tivity of 26% and specificity of 98%, with a positive predictive value of 29% and positive likelihood

139 sensitivity of 90%, a specificity of 71%, a positive predictive value of 32%, and a negative predict

140 The machine learning model has a mean positive predictive value of 34.6% [SD: 0.15] for flaggi

141 ng-2 a negative predictive value of 100% and positive predictive value of 42% in diagnosing recent ar

142 pecificity of 99.9% (95% CI, 99.9%-99.9%), a positive predictive value of 5.9% (95% CI, 2.6%-12.2%),

143 Our results show a Dice coefficient of 0.74, positive predictive value of 70% and negative predictive

144 182), a specificity of 93.1% (364 of 391), a positive predictive value of 74.8% (80 of 107), and a ne

145 parameter was below the threshold, it had a positive predictive value of 75%, and when both paramete

146 sitivity of 93.3%, a specificity of 76.5%, a positive predictive value of 77.8%, and a negative predi

147 s A (n = 511) and B (n = 127) demonstrated a positive predictive value of 78.4% for "clinical AERD,"

148 n indeterminate Quantiferon-CMV result had a positive predictive value of 83% and a negative predicti

149 84%, sensitivity of 82%, specificity of 93%, positive predictive value of 86%, and negative predictiv

150 sensitivity of 60.9%, specificity of 99.0%, positive predictive value of 86.5%, and negative predict

151 sensitivity of 100%, specificity of 92.31%, positive predictive value of 87.5%, and negative predict

152 vity of 71.6%, a specificity of 91.6%, and a positive predictive value of 87.8% for COVID-19.

153 sitivity of 94.5%, a specificity of 94.5%, a positive predictive value of 88.0%, and a negative predi

154 sitivity of 94.5%, a specificity of 94.4%, a positive predictive value of 88.0%, and a negative predi

155 sensitivity of 66.7%, specificity of 99.4%, positive predictive value of 88.9%, and negative predict

156 EVM and CMR together conferred a positive predictive value of 89% on EMB.

157 "negative" (n = 123) pattern demonstrated a positive predictive value of 89.6% and 86.2% for non-COV

158 had 77% sensitivity, 84% specificity, and a positive predictive value of 90% for >/=70% stenosis.

159 ventricular septal pacing with a cumulative positive predictive value of 91% (positive predictive va

160 g 272 who were successfully extubated, for a positive predictive value of 92%.

161 detectable with a specificity of 96.3% and a positive predictive value of 93.5% within the ASD subjec

162 9% (10 of 11), accuracy of 91.2% (31 of 34), positive predictive value of 95.5% (21 of 22), and a neg

163 96%), specificity of 79% (95% CI: 65%, 89%), positive predictive value of 96% (95% CI: 93%, 98%), and

164 ver operating characteristic analysis, and a positive predictive value of 97.2%.

165 iated with a secondary finding and the lower positive predictive value of a screening result compared

166 We report the patient-level CDR and the positive predictive value of AB-MR examinations after ne

167 Sensitivity, specificity, positive predictive value of abnormal findings at screen

168 ar to that of CEM or MBI, resulting in lower positive predictive value of additional biopsies (13 of

169 r molecular breast imaging, leading to lower positive predictive value of additional biopsies.

170 Negative predictive value, positive predictive value of an abnormal examination, an

171 In the study population, the positive predictive value of an OCT scan showing complet

172 The positive predictive value of AO for AO(post-BD) was 59.1

173 Cancer yield and positive predictive value of biopsies performed (PPV3) w

174 redictive value of recall (PPV1) percentage, positive predictive value of biopsies performed percenta

175 Positive predictive value of biopsy performed (PPV(3)) a

176 of abnormal findings at screening (PPV(1)), positive predictive value of biopsy performed (PPV(3)),

177 on and included cancer detection rate (CDR), positive predictive value of biopsy recommendation (PPV2

178 phic screening sensitivity, specificity, and positive predictive value of biopsy were 100% (95% CI: 5

179 ictive value of an abnormal examination, and positive predictive value of biopsy were 114 of 114 (100

180 specificity, negative predictive value, and positive predictive value of chest CT in the diagnosis o

181 e images (at least 1 of the 3 features), the positive predictive value of confocal microscopy was 87.

182 nography increased to 97.4% in 2015, and the positive predictive value of CT and combined CT and ultr

183 The negative predictive value and positive predictive value of DUS were 92% (95% CI, 90-95

184 l fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals w

185 The positive predictive value of new or progressive multiple

186 canonical positions (27%), and calculate the positive predictive value of pathogenicity for different

187 Positive predictive value of PlusoptiX was 69%.

188 external limiting membrane at 3 months is a positive predictive value of postoperative BCVA 6 months

189 Positive predictive value of Pretest Probability Score i

190 false-negative rate per 1000 women screened, positive predictive value of recall (PPV1) percentage, p

191 ecall rate, cancer detection rate [CDR], and positive predictive value of recall [PPV1]) was compared

192 FP) findings, biopsy, cancer detection rate, positive predictive value of recalls and biopsies, and h

193 itive infections identified, and improve the positive predictive value of results.

194 rtality, incidence, and treatments, plus the positive predictive value of screening mammography by ag

195 redictive value, but greater specificity and positive predictive value of spike ripples compared to s

196 The positive predictive value of the Pretest Probability Sco

197 To determine the positive predictive value of these tests and the percent

198 % predicted probability of hearing loss, the positive predictive value of this model was 83% and nega

199 The positive predictive value of tissue transglutaminase typ

200 The positive predictive value of ultrasonography increased t

201 COVID-19 is low, a large gap exists between positive predictive values of chest CT versus those of r

202 However, the low positive predictive values of symptoms elicited in prima

203 8), 96.4% specificity (95% CI, 95.5-97.2), a positive-predictive value of 41.6% (95% CI, 32.9-50.8),

204 ficity, negative predictive value (NPV), and positive predictive value-of IMPROD bpMRI for clinically

205 e of five), 93% specificity (132 of 142), 9% positive predictive value (one of 11), and 97% negative

206 rmine the relative performance (sensitivity, positive predictive value or PPV, and computational effi

207 on rate (P = .52 and P = .98, respectively), positive predictive value (P = .78), or sensitivity (P =

208 with negative predictive value (NPV) (100%), positive predictive value (PPV) (63.5%), and area under

209 The sensitivity, specificity, positive predictive value (PPV) and negative predictive

210 n metastasis-based detection sensitivity and positive predictive value (PPV) at a 50% confidence thre

211 ilies, an ASD-related CNV in a sibling has a positive predictive value (PPV) for ASD or atypical deve

212 The primary outcomes were the positive predictive value (PPV) for CRC and advanced ade

213 tive predictive value (NPV), specificity and positive predictive value (PPV) for that individual.

214 ompared with the individual assays), and the positive predictive value (PPV) improved substantially,

215 Visually the best negative (NPV) and positive predictive value (PPV) occurred when iPET was d

216 the best negative predictive value (NPV) and positive predictive value (PPV) occurred when iPET was d

217 determine the sensitivity, specificity, and positive predictive value (PPV) of (18)F-DCFPyL PET/CT b

218 omprising BATF2, GBP5, and SCARF1 achieved a positive predictive value (PPV) of 23% for progression t

219 ed a sensitivity of 86%, specificity of 58%, positive predictive value (PPV) of 57% and negative pred

220 ng/l identified patients at high risk with a positive predictive value (PPV) of 76.8% (95% CI: 68.9%

221 An accuracy of 98%, positive predictive value (PPV) of 86% and negative pred

222 h a histopathology report of SPs, yielding a positive predictive value (PPV) of 95% (95%CI = 89-98%).

223 retation rate, 12.6% (95% CI: 12.5%, 12.7%); positive predictive value (PPV) of a biopsy recommendati

224 additional imaging recommendation rate, and positive predictive value (PPV) of biopsy, using invasiv

225 For event occurrence, positive predictive value (PPV) of claims versus adjudic

226 mate the negative predictive value (NPV) and positive predictive value (PPV) of ctHPVDNA surveillance

227 Diagnostic performance of endoscopy and positive predictive value (PPV) of endoscopic features f

228 re-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctl

229 We estimated the positive predictive value (PPV) that a new diagnosis of

230 cular invasion, although the specificity and positive predictive value (PPV) were 100%.

231 ancer detection rate (CDR), recall rate, and positive predictive value (PPV) were calculated for each

232 ficity, negative predictive value (NPV), and positive predictive value (PPV) were calculated separate

233 Referral, unable readings, and positive predictive value (PPV) were reported.

234 pecificity (95% CI, 82%-100%) and up to 100% positive predictive value (PPV) with estimated negative

235 PV to predict hHSILs in normal cytology were positive predictive value (PPV), 29.3% (25.6%-33.3%); ne

236 In our cohort, this algorithm achieved 94% positive predictive value (PPV), 53% negative predictive

237 97%); specificity, 93% (95% CI, 91% to 94%); positive predictive value (PPV), 89% (95% CI, 86% to 91%

238 study was to assess the detection rate (DR), positive predictive value (PPV), and correct detection r

239 which is based on sensitivity, specificity, positive predictive value (PPV), and negative predictive

240 outcomes were the sensitivity, specificity, positive predictive value (PPV), and negative predictive

241 racteristic curve; sensitivity, specificity, positive predictive value (PPV), and negative predictive

242 lture, the overall sensitivity, specificity, positive predictive value (PPV), and negative predictive

243 The sensitivity, specificity, positive predictive value (PPV), and negative predictive

244 Sensitivity, specificity, positive predictive value (PPV), and negative predictive

245 Sensitivity, specificity, positive predictive value (PPV), and negative predictive

246 th nested PCR, the sensitivity, specificity, positive predictive value (PPV), and negative predictive

247 Sensitivity, specificity, positive predictive value (PPV), and negative predictive

248 The sensitivity, specificity, positive predictive value (PPV), and negative predictive

249 genes estimated as sensitivity, specificity, positive predictive value (PPV), and negative predictive

250 Sensitivity, specificity, positive predictive value (PPV), and negative predictive

251 haracteristics, particularly specificity and positive predictive value (PPV), by mapping plasma miR37

252 rformance metrics (sensitivity, specificity, positive predictive value (PPV), negative predictive val

253 d to summarize the sensitivity, specificity, positive predictive value (PPV), negative predictive val

254 Positive predictive value (PPV), negative predictive val

255 We calculated the sensitivity, specificity, positive predictive value (PPV), negative predictive val

256 ll-known methods, especially in terms of the positive predictive value (PPV), which indicated the con

257 .3%; sensitivity: 88.9%; specificity: 72.4%; Positive predictive value (PPV): 50%; Negative predictiv

258 oral metronidazole treatment, with both 90% positive predictive values (PPV) and 74% negative predic

259 was 88.7% (95% CI 81.7-93.8%) for grader 1 (positive predictive value [PPV] 59.1%) and 92.5% (95% CI

260 udy was to evaluate the tissue biopsy-proven positive predictive value (PPV3) for BI-RADS 4 (and its

261 ovarian cancer consistently resulted in low positive predictive values (PPVs) and false-positive rat

262 We calculated sensitivity and positive predictive values (PPVs) by comparing the algor

263 Positive predictive values (PPVs) for any neoplasm 6 mm

264 Sensitivity, specificity, positive predictive values (PPVs), and NPVs were calcula

265 ated using flexible parametric modeling, and positive predictive values (PPVs), sensitivity, specific

266 ific CNV callers, resulting in a much higher positive predictive value (precision).

267 dence interval [CI]: 86.8% to 94.1%) and the positive predictive value, sensitivity, and specificity

268 false-negative rate, cancer detection rate, positive predictive value, sensitivity, and specificity

269 etection rate, abnormal interpretation rate, positive predictive values, sensitivity, and specificity

270 f sensitivity, specificity, and negative and positive predictive values supported some potential of A

271 utoff to >=1.50 D in both eyes increased the positive predictive value to 96%.

272 As a consequence, the positive-predictive value toward ICM configuration was s

273 cificity, cancer detection and recall rates, positive predictive values, tumor size, histologic featu

274 who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92

275 rence standard was 85.4% (35 of 41), and the positive predictive value was 100% (35 of 35).

276 The positive predictive value was 11% and the negative predi

277 The positive predictive value was 17.4% (95% CI, 14.2 to 21.

278 The positive predictive value was 20.6%.

279 sensitivity was 100%, specificity was 51.1%, positive predictive value was 21.8%, and negative predic

280 nodal staging was 57.8% (44.8-70.1%) and the positive predictive value was 71% (60.6-79.9%).

281 %, sensitivity was 83%, specificity was 84%, positive predictive value was 77%, negative predictive v

282 value of PCR was found to be 100%, while the positive predictive value was 79%.

283 y for the three readers was 60%-65%, and the positive predictive value was 86%-96%.

284 The positive predictive value was 86.3% (95% CI, 80.4 to 90.

285 On a per-patient basis, positive predictive value was 93.3% (95% CI, 77.6-99.2%)

286 On a per-patient basis, positive predictive value was 93.3% (95% confidence inte

287 ecificity was 97.8% (95% CI, 96.0 to 98.8%), positive predictive value was 94.0% (95% CI, 89.3% to 96

288 he cut-off was 97.1%, specificity was 97.6%, positive predictive value was 97.1%, and the negative pr

289 ysis using data after the randomized period, positive predictive value was higher (25.8% vs. 18.2%) a

290 The positive predictive value was low (23%).

291 TLs, the sensitivity was high (67%), but the positive predictive value was low, as only 8% of TWAS as

292 s. 4-5; DeltaSUV(max) > 66% vs. <= 66%), the positive predictive value was significantly lower with D

293 Statistical analysis for positive predictive values was modeled using the receive

294 specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, r

295 Recall, screen-detected breast cancer, and positive predictive value were analyzed for consecutivel

296 Sensitivity and positive predictive values were <90% for all biomarkers.

297 acy, including sensitivity, specificity, and positive predictive value, were calculated for the stand

298 predicts 53 serious adverse events with high positive predictive values where well-characterized targ

299 l stability showed high concordance and high positive predictive value with clinical success at PTE.

300 s' follow-up, there was a modest increase in positive predictive value with the higher thresholds (3.