ライフサイエンスコーパス: cutoff

1 nths; '+' refers to ongoing response at data cutoff).

2 cultured specimens (99% nucleotide identity cutoff).

3 one or more dose of patisiran up to the data cutoff.

4 n the period between first infusion and data cutoff.

5 12-month efficacy assessments as of the data cutoff.

6 .3 months (IQR 5.1-15.7) at the time of data cutoff.

7 tervals yielded 166 cm as the optimal height cutoff.

8 atient subgroups defined by the prespecified cutoffs.

9 ve predictive value for the proposed optimal cutoffs.

10 motor worsening, using clinically meaningful cutoffs.

11 s DDIR negative or positive using predefined cutoffs.

12 xplored the performance of varying LTBI test cutoffs.

13 r borderline/clinical ranges using validated cutoffs.

14 ride and/or lipase levels exceeding protocol cutoffs.

15 false discovery rate <0.05; log-fold change cutoff = 0) from 36,007 expressed Populus gene models.

16 showed 90% sensitivity and 88% specificity (cutoff, 0.97).

17 owed 94% sensitivity with a 73% specificity (cutoff, 0.97).

18 (IE-1) antigen spot counts (SPCs) were >100 (cutoff 1) or when the IE-1 and phosphoprotein 65 antigen

19 At data cutoff, 108 patients with previously treated endometrial

20 on baseline platelet count into two groups (cutoff, 150 x 10(9) /L) demonstrated a statistically sig

21 chieved 92% sensitivity and 97% specificity (cutoff, 18 mum), whereas CPTR showed 90% sensitivity and

22 hieved 94% sensitivity and 100% specificity (cutoffs, 19 mum and 20 mum, respectively), whereas CPTR

23 SPCs were both >100 SPCs per 250 000 cells (cutoff 2), and a low CMV-CMI when SPCs were below these

24 d by an ultrasensitive toxin assay (clinical cutoff, 20 pg/mL).

25 Among the 34.9% with values between the cutoffs, 26.1% had advanced fibrosis.

26 Between Oct 26, 2015, and Nov 16, 2018 (data cutoff), 46 patients were enrolled in the dose-escalatio

27 quartiles based on hospital occupancy rate (cutoffs: 48.1%, 59.4%, 68.2%).

28 Between July 2012 and clinical cutoff, 50 patients crossed from the placebo to the pert

29 Using height <166 cm as the cutoff, 72% of women and 9% of men met criteria.

30 The prolonged critical illness cutoff (90th percentile length of stay) was greater than

31 lternative assays for hs-cTnI or hs-cTnT, no cutoff achieved the target performance: hs-cTnT concentr

32 cence in-situ hybridization in 2 age groups (cutoff age, 18 months) and were quality controlled by th

33 rformance in reference to the manufacturer's cutoff and also to a threshold chosen to achieve sensiti

34 Two cutoffs and 3 different periods were identified: cases 1

35 African populations, with use of established cutoffs and mean 25(OH)D concentrations.

36 ls, categorized according to pre-established cutoffs, and analyzed according to the severity of the d

37 on methods, technical and clinical detection cutoffs, and clinical significance of LA-DRVs in antiret

38 ts either available burden-free to the user (cutoff approach) or partially responsible for the emissi

39 Using VCTE dual cutoffs, approximately two-thirds of patients could avoi

40 At the data cutoff (April 2, 2018), two patients remained on study t

41 At data cutoff (April 3, 2020), median follow-up was 20.6 months

42 Median follow-up at data cutoff (April 30, 2018) was 44 months (IQR 38-49) in the

43 Median follow-up at data cutoff (April 5, 2019) was 35.7 months (IQR 24.9-43.6) i

44 At data cutoff (April 8, 2019), median follow-up of 105 enrolled

45 el for End-Stage Liver Disease (MELD) with a cutoff at 15 (<15 and >=15).

46 Importantly, a vWF-Ag cutoff at 413% identified patients at risk for death wit

47 dds ratio (OR) than the previously published cutoff at a MELD-Na of 20 points (vWF-Ag, OR = 10.873, 9

48 44 single-nucleotide polymorphism score and cutoff at the 30th percentile.

49 alysis of waitlist mortality to find optimal cutoffs at 3, 6, or 12 months.

50 At the data cutoff (Aug 7, 2019), the median follow-up period was 12

51 At data cutoff (August 31, 2019), 258 patients (n = 184 GIST) we

52 ght to identify a clinically relevant height cutoff below which waitlist mortality increases signific

53 We identified a single LFI cutoff by evaluating the fit of the competing risk model

54 ate for more than 250 cycles (1500 h) with a cutoff capacity of 300 mA h g(-1) .

55 A single cutoff concentration >60 ng/l identified patients at hig

56 A single cutoff concentration <3 ng/l at presentation identified

57 ason score of less than or equal to 3 + 4, a cutoff could be established for detecting a Gleason scor

58 olics) > k(gingerols), resulting in multiple cutoff criteria and predicted shelf-lives (SL(predicted)

59 respectively, of which ~one-third failed the cutoff criterion for either treatment alone.

60 een reported since the primary clinical data cutoff date (April 17, 2018).

61 At the data cutoff date (Feb 21, 2020), the median follow-up was 53.

62 At the data cutoff date for this analysis (May 31, 2018) the efficac

63 At the data cutoff date for this analysis (May 31, 2018), 41 (77%; 9

64 e 5 years of follow-up with an analysis data cutoff date of Aug 31, 2017.

65 At the cutoff date of July 20, 2017, a total of 34 patients (27

66 At the data cutoff date on March 30, 2020, median follow-up duration

67 of June 21, 2019 (the primary analysis data cutoff date), 30 (31%; 97.5% CI 20.8-42.6) of 97 patient

68 eligibility, and as of the Jan 31, 2019 data cutoff date, 185 patients were randomly assigned to ivos

69 e, defined as time to death or time to study cutoff date.

70 SST) and updated safety were analysed (visit cutoff Dec 31, 2017).

71 At the second interim analysis (data cutoff, Dec 11, 2019), median follow-up was 25.9 months

72 Serotype positivity was based on cutoffs determined by nonparametric statistical evaluati

73 al quadratic regression around the 90-minute cutoff did not reveal evidence of a significant disconti

74 Even at the highest cutoffs evaluated (15mm TST, >=1.00 IU/mL QFT, >=8 spots

75 FFR values from above to below the clinical cutoff FFR value of 0.80 in 1 out of 5 measurements.

76 g several methods for determining an optimal cutoff for (18)F-AZD4694 PET positivity.

77 The optimal cutoff for (64)Cu-DOTATATE SUV(max) was 43.3 for predict

78 -based contrast administration, lowering the cutoff for administering prophylaxis to glomerular filtr

79 ers, no studies have assessed a quantitative cutoff for amyloid-beta positivity using (18)F-AZD4694 P

80 (ROC) curve was carried out to identify the cutoff for completion of the learning curve.

81 oS > 4 days vs. LoS <= 4 days, using a 4 day cutoff for early discharge, ED) regression.

82 younger and using each biomarker median as a cutoff for high- and low-risk groups.

83 ity: 97%; specificity: 82%), and the optimal cutoff for IR diagnosis was 5.9 (sensitivity: 71%; speci

84 A SPISE of 6.4 was the optimal cutoff for IR diagnosis; however, sensitivity and specif

85 In males, the optimal cutoff for MetS diagnosis was 5.0 (sensitivity: 97%; spe

86 alysis was performed to identify the optimal cutoff for prognostic threshold of AVA.

87 The optimal flow rate cutoff for prognostic threshold was 210 ml/s.

88 rdized uptake value method; the optimal TMTV cutoff for progression-free (PFS) and overall survival (

89 c analysis was used to determine the optimal cutoff for quantitative variables, and Kaplan-Meier surv

90 At data cutoff for the analysis (Aug 9, 2019), the median follow

91 This date was set as the clinical cutoff for the primary analysis.

92 A cutoff for the tumor SUV(max) could be established with

93 Clinical cutoff for this analysis was Nov 23, 2018.

94 Using an optimised cutoff for this biomarker, sensitivity was 86% and speci

95 The data cutoff for this interim analysis was Sept 3, 2018.

96 The optimal LFI cutoff for waitlist mortality was 4.4 at 3 months and 4.

97 n using manufacturer-defined cycle threshold cutoffs for all channels was 88.7% (95% CI 83.1-94.3), a

98 The optimal prognostic cutoffs for burden of COVID-19 pneumonia as determined b

99 on as there are no accepted quantitative PCR cutoffs for diagnosing respiratory viral infections.

100 erating-characteristic analysis to determine cutoffs for differentiation between low- and intermediat

101 ty, specificity, PPV, and NPV at the optimal cutoffs for each classifier were 94.2%, 96.9%, 97%, and

102 WHO cutoffs for fast breathing and hypoxaemia overlap with R

103 We determined the utility of dual cutoffs for noninvasive biomarkers to exclude and confir

104 ific models and adjust the PS and optimal PS cutoffs for overfitting.

105 eading some centers to employ subjective age cutoffs for potential SLK recipients.

106 osis equally well but give different optimal cutoffs for risk stratification.

107 Using ROC-curve analysis, serum HCV-RNA cutoffs for ruling in/out any HCV+SS were established at

108 At the extreme cutoffs for ruling-out and ruling-in inducible VT, RV LG

109 evaluated for statistical significance, and cutoffs for these parameters were determined to maximize

110 Dual cutoffs for three noninvasive biomarkers (aspartate amin

111 Analysis of ELISA performance at various cutoffs found that the manufacturers' recommended cutoff

112 k height in blip transients if the band-stop cutoff frequencies are compatible with the nanoparticle/

113 Notably, the estimated cutoff frequency is lower for low- than for high-spontan

114 PEDOT:PSS hydrogels were shown to alter the cutoff frequency of the analog filter, indicating a pote

115 The estimated cutoff frequency varies with spontaneous rate, revealing

116 the vicinity of the free space ordinary mode cutoff frequency, we estimate the characteristic plasma

117 were converted dichotomously with optimized cutoffs from ROC analyses, we achieved 99.5% concordance

118 tivity analyses explored a more conservative cutoff >5 units/mL.

119 -2 (with cutoff >=2) followed by PHQ-9 (with cutoff >=10) had similar sensitivity but higher specific

120 on diagnoses, the combination of PHQ-2 (with cutoff >=2) followed by PHQ-9 (with cutoff >=10) had sim

121 t moderate levels of psychological distress (cutoff >=5 on the Kessler 6).

122 for all three LTBI tests using standard U.S. cutoffs (>=5mm TST, >=0.35 IU/mL QFT, >=8 spots TSPOT).

123 ts, but its value is limited as quantitative cutoffs have only been established for a few bacterial p

124 ry tumor site, and tumor grade, the SUV(max) cutoff hazard ratio was 0.50 (range, 0.32-0.77) for PFS.

125 ts with laboratory values exceeding protocol cutoffs, implementation of the best practice advisory re

126 Cutoff-independent discrimination performance remained l

127 At the second interim analysis (data cutoff Jan 2, 2019), median follow-up was 18.5 months (I

128 At data cutoff (Jan 27, 2020), median follow-up for survival was

129 At the time of data cutoff (Jan 31, 2020), the median duration of follow-up

130 low-up for progression-free survival at data cutoff (July 10, 2018) was 12.4 months (IQR 8.3-21.7) in

131 were enrolled at least 26 weeks before data cutoff (June 27, 2019), and safety was assessed in all p

132 1066 participants were treated as of data cutoff (June 27, 2019), of whom 805 (76%) were evaluable

133 For all cutoffs, laser goniopuncture (P < 0.001), needling (P <

134 d and compared with the conventional D-dimer cutoff level of 500 ug/L.

135 when compared with the conventional D-dimer cutoff level to rule out thromboembolic disease (< 500 u

136 glomerular filtration rate-adjusted D-dimer cutoff levels (> 333 ug/L [estimated glomerular filtrati

137 D-Dimer cutoff levels adjusted for renal dysfunction appear feas

138 glomerular filtration rate-adjusted D-dimer cutoff levels are applied.

139 Furthermore, adjusted D-dimer cutoff levels seem reliable in patients with acute kidne

140 ics were also observed when adjusted D-dimer cutoff levels were applied in patients with acute kidney

141 glomerular filtration rate-adjusted D-dimer cutoff levels.

142 entation patterns but also overcome low mass cutoff limitations in ion trap mass analyzers.

143 ensitivity typically due to early absorption cutoff, low responsivity, and/or large dark/noise curren

144 no objective data to justify a specific age cutoff <70 years among potential SLK recipients with CKD

145 resent the final confirmatory analysis (data cutoff May 31, 2017).

146 At data cutoff (May 31, 2019), at a median follow-up of 6.3 mont

147 Using permissive cutoffs, modified reference genomes are found to alter a

148 drug substance by applying molecular weight cutoff (MWCO) filtration step followed by shotgun proteo

149 At data cutoff (Nov 16, 2018), 38 (46%) of 82 patients in the sa

150 At data cutoff (Oct 1, 2018), after a median follow-up of 11.0 m

151 At a pre-selected cutoff of >80% SN and SP for the affiliated PancRISK sco

152 l pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the re

153 An astigmatism cutoff of >=1.50 diopters (D) in either eye correctly pr

154 The use of a pleocytosis cutoff of >=10 cells/mm(3) would have missed an addition

155 tead of the current high-risk classification cutoff of >=3 adenomas or any adenoma with villous growt

156 At an RAI cutoff of >=37, the positive predictive values for 30- a

157 A baseline stool Shannon diversity cutoff of <2 had optimal operating characteristics for p

158 A MUAC cutoff of <=112 mm performed well in detecting severe wa

159 The optimal MUAC cutoff of <=112 mm yielded the highest Youden index of 0

160 Using an OSFI cutoff of 0.300, we identified a small group (19% of the

161 2Ae patients developing CKD stage 3 using a cutoff of 0.5 for predicting case status was better cali

162 Using a probability cutoff of 0.5 to assign class, inflammatory biomarkers (

163 alysis yielded an optimal (18)F-FET TBR(max) cutoff of 1.95 (sensitivity, 70%; specificity, 71%; accu

164 ent US Food and Drug Administration-required cutoff of 12.0 for designation of high-titer convalescen

165 The hs-cTnI cutoff of 2.5 ng/L provided an NPV of 70% (95% CI, 64% t

166 An hs-cTnI cutoff of 2.5 ng/L, derived previously in mostly asympto

167 CI, 0.85 to 0.93) to detect dementia using a cutoff of 23 or less or 24 or less (15 studies, n = 12 7

168 An optimal cutoff of 278.9 mL yielded accuracy of 84%, sensitivity

169 We identified an optimal LFI cutoff of 4.4 (95% confidence interval [CI], 4.0-4.8) fo

170 Using a cutoff of 80% predicted probability of hearing loss, the

171 questions are reported as percentages with a cutoff of alpha = 0.05 for significance.

172 fication, the currently used Deauville score cutoff of an uptake above that of the liver seems to be

173 0 transcription factor genes expressing at a cutoff of FPKM >= 1 in the glandular trichomes of N. tab

174 and glandular trichomes, respectively, at a cutoff of FPKM >= 1.

175 sensitivity = 1, specificity = 0.95) using a cutoff of K(i) = 5.3 x 10(-3) mL/cm(3)/min, while k(3) a

176 s than 30 nmol/L; 34.22% (26.22-43.68) for a cutoff of less than 50 nmol/L; and 59.54% (51.32-67.50)

177 an 50 nmol/L; and 59.54% (51.32-67.50) for a cutoff of less than 75 nmol/L.

178 tatus was 18.46% (95% CI 10.66-27.78) with a cutoff of serum 25(OH)D concentration less than 30 nmol/

179 ty increases with frequency up to a low-pass cutoff of ~70-80 Hz.

180 decreased CMV infection by PCR at viral load cutoffs of >=1000 and >=10 000 IU/mL in the CMV IVIG arm

181 ng to the frequency of visits with IOP below cutoffs of 21 mmHg, 18 mmHg, and 15 mmHg over time.

182 VCTE cutoffs of 5.0 kPa or less (to exclude) and 8.8 kPa or g

183 ucts with energy and nutrients exceeding the cutoffs of the law (i.e., products "high in") were compa

184 ts enrolled by at least 26 weeks before data cutoff, of whom 790 (75%) were evaluable for TMB and inc

185 At the time of data cutoff on Aug 6, 2019, median follow-up among survivors

186 re, we present an interim analysis with data cutoff on March 29, 2019.

187 Data presented are from the data cutoff on May 22, 2019.

188 up was 13.2 months (IQR 7.3-20.4) as of data cutoff on Nov 30, 2018.

189 d according to treatment received, with data cutoff on Nov 4, 2020.

190 was 10.5 months (range 0.2-20.4) as of data cutoff on Nov 8, 2017.

191 At the data cutoff on Sept 24, 2018, 126 (92%) of 137 patients from

192 More stringent cycle threshold cutoffs on selected channels in Xpert HPV improve specif

193 Shifting cycle threshold cutoffs on these three channels allowing sensitivity to

194 ther HPV type restriction and more stringent cutoffs on Xpert HPV optimise performance characteristic

195 er the CD49d+ subpopulation exceeded the 30% cutoff or not, experienced clinical behavior similar to

196 prevalence, only FibroTest high specificity cutoff plus VCTE was equivalent to or better than LB.

197 patients with AF had ECV below the fibrosis cutoff point (32.8% when converted from MOLLI T1 mapping

198 The optimal cutoff point for relative decline in MRI-PDFF for histol

199 We classified ADHD using recommended cutoff points for each instrument used to derive symptom

200 yses using 0-1 versus 2-6 and 0-2 versus 3-6 cutoffs (predefined secondary analysis).

201 The optimal cutoff predicting success with a preoperative cylinder w

202 tperformed different clinically relevant IgE cutoffs, predicting allergy status on an "unseen" set of

203 table mortality while avoiding arbitrary age cutoffs, premature avertable mortality from NCDs is a ro

204 A cutoff probability score from the training data was esta

205 HIV serology was quantified by signal-to-cutoff ratio (S/CO).

206 ho VITROS IgG assay revealed a median signal/cutoff ratio of 24.0 for transfused units, a value far e

207 No hs-cTnI cutoff reached both performance characteristics predefin

208 rformance remained low for both methods, but cutoff-related analyses showed promising results.

209 The optimal cutoff reliably classified patients (including patients

210 The most common body mass index (BMI) cutoff remains 35 kg/m(2) , and fewer programs now consi

211 Using the 66% DeltaSUV cutoff reported previously, NPV and PPV were 80% and 76%

212 DeltaSUV at the 48.9% cutoff, reported for the first time here, produced 100%

213 Cutoffs resulting in statistically significant (P < 0.00

214 shift from above to below the 0.80 clinical cutoff, resulting into a change of the treatment strateg

215 red with reference color plots, and the SSIM cutoff score was optimized to distinguish adenosine.

216 At cutoff scores at which the machine learning model and GB

217 y but higher specificity compared with PHQ-9 cutoff scores of 10 or greater alone.

218 re 0.91 (0.88-0.94) and 0.67 (0.64-0.71) for cutoff scores of 2 or greater and 0.72 (0.67-0.77) and 0

219 nd 0.72 (0.67-0.77) and 0.85 (0.83-0.87) for cutoff scores of 3 or greater.

220 can be shifted to programme implementers and cutoffs selected according to resource availability and

221 ative approach for network reconstruction: a cutoff selection algorithm that maximizes the overlap of

222 2 patients had an objective response at data cutoff (Sept 17, 2018).

223 At data cutoff (September 26, 2018), the minimum follow-up was 2

224 At data cutoff (September of 2019), all patients discontinued th

225 Technical detection cutoffs showed a 4 log range (0.001%-10%); 42/103 (40.8%

226 opositive, the ELISA (with a 0.9 index value cutoff) showed 95.2% sensitivity, 93.4% specificity, 6.6

227 Based on the surrogate protective antibody cutoff, significantly more participants were protected a

228 the competing risk models, searching for the cutoff that gave the best model fit (as judged by the ps

229 We sought to identify an optimal LFI cutoff that predicts waitlist mortality.

230 ng samples based only on more severe symptom cutoff thresholds will likely have little impact on popu

231 by quality and more accurately determine QC cutoff thresholds.

232 This provides a practical cutoff time window for community-based surveillance stud

233 for glasses 93% of the time; increasing this cutoff to >=1.50 D in both eyes increased the positive p

234 inguished quartile 3 from quartile 4 was the cutoff to distinguish medium-risk and high-risk populati

235 core for quartiles 1-2 was identified as the cutoff to identify low-risk patients; and the score that

236 ficity attained by modifying cycle threshold cutoffs to define screen-positive results.

237 2) Youden's J statistic to select optimum PS cutoffs to maximize diagnostic performance using receive

238 c curve identified >15.5 mm as the best size cutoff value (area under the curve 0.747).

239 This is also the epidemiological cutoff value (ECV) (ie, the end of the wild-type suscept

240 For prediagnostic cases below cutoff value for CA19-9, the combination with LRG1 and T

241 ic curves were used to determine a potential cutoff value for discharge home.

242 The cutoff value for ICG Ingress was 106.23 AU with sensitiv

243 ered with histology to determine the optimal cutoff value for strut coverage by OCT which was defined

244 ction of mortality, assessed the appropriate cutoff value for the dichotomized score, and compared th

245 The cutoff value for the extensively validated 30% of positi

246 elded an optimal mean fluorescence intensity cutoff value for tumor detection of 5,775 (sensitivity o

247 Use of a fat fraction cutoff value greater than 1.8% yielded a sensitivity of

248 Of 3 cutoff values, the cutoff value of >=40 mum yielded the best sensitivity (9

249 estionnaire (RBDSQ) and dichotomized using a cutoff value of >=6.

250 Using a higher C(T) cutoff value of <=40, 90% sensitivity was achieved for u

251 a weight for length z score (WLZ) below the cutoff value of -3 SDs from the median as per the WHO 20

252 A cutoff value of 20 points identified patients in remissi

253 At the model-predicted cutoff value of 3,000 ng/mL, sensitivity was 100%, speci

254 A cutoff value of 50 points identified patients in remissi

255 with cfDNA at 24 h concentrations above the cutoff value of healthy patients (>850 ng/ml) had a sign

256 present study was to assess the appropriate cutoff value of neointimal thickness of stent strut cove

257 ss >=40 mum by OCT yielded the most accurate cutoff value to identify stent strut coverage validated

258 racteristic analysis showed that the optimal cutoff value to predict lung tissue recruitment for the

259 mor detection, a mean fluorescence intensity cutoff value was determined from the formalin-fixed tiss

260 y segments and the FFR value is close to the cutoff value.

261 The calculated optimal cutoff values during the course of disease from the eval

262 Optimal cutoff values for distinguishing advanced fibrosis were

263 max (bSUVmax) and HT risk was assessed using cutoff values for SUVmax >10 and >20.

264 nfections to validate the test and determine cutoff values for use in a cascading diagnostic algorith

265 After successful internal validation of the cutoff values generated by the training cohort for DFS (

266 best predictive value for ICU mortality with cutoff values less than or equal to 1.25 arbitrary perfu

267 Screening cutoff values of 8-OHdG >50 ng/mg of creatinine or urine

268 g the resolution of OCT is 10 to 20 mum, the cutoff values were assessed at >=20, >=40, and >=60 mum.

269 Epidemiological cutoff values were calculated and determined to be 256 m

270 For this reason, 2 different cutoff values were calculated for age groups 4-10 and 11

271 Cutoff values were then determined by using Youden's Ind

272 ed likelihood ratios of result intervals and cutoff values with 100% negative (NPV) and positive (PPV

273 Of 3 cutoff values, the cutoff value of >=40 mum yielded the

274 most of the changes took place close to the cutoff values, with only few exceptions of overall left

275 spectively, which is well below the clinical cutoff values.

276 acity losses in LTMOs, is independent of the cutoff voltage during delithiation and it is a reversibl

277 Median follow-up at the time of data cutoff was 2.3 months (IQR 1.6-3.5).

278 dian baseline TMTV was 238 cm3; optimal TMTV cutoff was 220 cm3.

279 determined that the most effective DeltaSUV cutoff was 48.9% and that when combined with 5-PS assess

280 An optimal cutoff was derived by using a radial basis function-base

281 However, no significant cutoff was found for prediction of OS.

282 a median follow-up time of 44.6 months; data cutoff was January 20, 2020.

283 Data cutoff was Oct 10, 2018, when the fully enrolled cohort

284 membrane selectivity, and the particle size cutoff was reduced from 35 to 10 nm post-treatment with

285 When a previously defined optimal cutoff was used, the BAT diagnosed PA with 98% specifici

286 (p = 0.002), external validation using these cutoffs was successful for DFS (p = 0.002) but not for t

287 d 5th percentiles for SpO(2) against the WHO cutoffs, we found that the proportion of false positives

288 above the receiver operating characteristics cutoff were deceased whereas only 28% below the cutoff w

289 off were deceased whereas only 28% below the cutoff were deceased.

290 ensitivity and specificity of the positivity cutoffs were assessed in a clinical validation, using ur

291 food and beverage groups in which regulatory cutoffs were below the 75th percentile of the nutrient o

292 ndiseased prostate segments, and optimal SUV cutoffs were calculated using the Youden index for each

293 Three intraocular pressure (IOP) success cutoffs were defined: 18 mmHg or less and 20% reduction,

294 fs found that the manufacturers' recommended cutoffs were diagnostically specific, i.e., produced no

295 Subtherapeutic cutoffs were: rifampicin <8 ug/mL, isoniazid <3 ug/mL, a

296 mental volume waveguide mode that exhibits a cutoff width is identified, which, interestingly, can be

297 y hybridized surface mode does not exhibit a cutoff width, and thus enables linear waveguiding of the

298 ar-HIT-IgG) and titer >=16 (ID-H/PF4-PaGIA); cutoffs with 100% NPV were <0.13 U/mL and <=1, respectiv

299 Cutoffs with 100% PPV for positive HIPA were >3.0 U/mL (

300 Cutoffs with the highest accuracy in predicting HBeAg se