ライフサイエンスコーパス: nomogram

1 .72 for Duke Treadmill Score; 0.75 for Lauer nomogram).

2 mor number, which were incorporated into the nomogram.

3 alized Metabolic Surgery (IMS) score using a nomogram.

4 d onto 127 trials were analyzed to build the nomogram.

5 to select variables for construction of the nomogram.

6 gnostic factors, and performance of proposed nomogram.

7 ctors except dose were included in the final nomogram.

8 sification system, and the 5-gene score in a nomogram.

9 ctioning parathyroid glands based on the WIN nomogram.

10 to mastectomy and were incorporated into the nomogram.

11 dated Memorial Sloan Kettering Cancer Center nomogram.

12 ate analysis with a validated gastric cancer nomogram.

13 ots were used to evaluate the performance of nomogram.

14 ated for the seven variables in the original nomogram.

15 of the Malawi Adult Meningitis Score (MAMS) nomogram.

16 f PSA-defined recurrence, based on a popular nomogram.

17 ciated carcinoma in the form of a predictive nomogram.

18 dmill scores as low risk on the basis of the nomogram.

19 xplained by, the institution of a predictive nomogram.

20 recipient variables outside the established nomogram.

21 f overall survival was used to construct the nomogram.

22 ge, were selected for the distant metastases nomogram.

23 ltivariable model for the distant metastases nomogram.

24 w and prospective validation of a predictive nomogram.

25 d logistic regression and was presented on a nomogram.

26 evaluate the clinical usefulness of the two nomograms.

27 d procedure for variables selection for both nomograms.

28 is multivariable model was used to develop a nomogram-a weighted tool to calculate 2- and 4-year prob

29 We depicted the results in a series of nomograms accounting for age, comorbidities, and cyst si

30 The nomogram accurately predicted mortality, including very

31 The nomogram accurately predicted readmission (C statistic =

32 The nomogram accurately predicts RFS after resection of loca

33 These nomograms accurately predict OS and DFS.

34 cancer site (colon vs. rectum), and age, the nomogram achieved a concordance index of 0.61, statistic

35 ction of biochemical failure with the Kattan nomogram after external-beam radiation therapy for prost

36 The constructed nomograms allow a comparison of predicted breast cancer-

37 The nomogram also had very good calibration.

38 A nomogram and a browser-based software tool were built fr

39 nts), to develop 2 prediction instruments, a nomogram and a transfusion score, which can be easily im

40 utperforms the two widely used tools, Kattan nomogram and CAPRA-S score in a head-to-head comparison

41 ots, survival distributions predicted by the nomogram and observed by the Kaplan-Meier method were si

42 The areas under the ROC curve for the Kattan nomogram and the model incorporating MR imaging findings

43 Models were converted to nomograms and a web-based tool to determine individual r

44 velopment of prognostic indicators including nomograms and can be analyzed by Bayesian Belief Network

45 he prognostic value of age- and gender-based nomograms and categorical definitions of impaired exerci

46 er refractive outcomes, including the use of nomograms and mathematical models.

47 Many of the current nomograms and scoring algorithms have been externally va

48 .70 for Duke Treadmill Score; 0.74 for Lauer nomogram) and men (0.72 for Duke Treadmill Score; 0.75 f

49 tient selection, development of a predictive nomogram, and advances in mutational analysis represent

50 Memorial Sloan Kettering Cancer Center GIST nomogram, and American Joint Committee on Cancer gastric

51 be guided using novel Markov-based clinical nomograms, and depends on age, cyst size, comorbidities,

52 The nomogram appears useful for risk stratification in clini

53 ferences in the utility of the commonly used nomograms are briefly outlined.

54 Today, most reported nomograms are inconsistent with both established definit

55 t and validation of transcutaneous bilirubin nomograms are needed.

56 Our nomograms are reliable prognostic methods that can be us

57 Nomograms are widely used as prognostic devices in oncol

58 Nomograms are widely used for cancer prognosis, primaril

59 Instead of constructing nomograms, authors should develop software, such as pred

60 A simple nomogram based on easily obtained pretest and exercise t

61 Conclusion This simple nomogram based on the ALBI grade offers personalized lon

62 developed a user-friendly prediction model (nomogram) based on a large data set to assist in predict

63 The two nomograms both performed well in terms of discrimination

64 A nomogram built from a parametric survival model from the

65 d likelihood of residual axillary disease by nomogram but an observed axillary LR of 2%.

66 We assessed performance of the nomogram by calculating concordance statistics and asses

67 The nomogram can be applied only to patients with a normal e

68 ry angiography are associated with PH, and a nomogram can be created that may facilitate identificati

69 ts occurrence can be predicted if a clinical nomogram can be developed, thus allowing for selection o

70 This nomogram can be used to accurately predict a patient's r

71 The constructed nomogram can guide prognostication in clinical practice

72 These nomograms can be offered to clinicians to improve their

73 These nomograms can be used to better estimate individual and

74 Contemporary nomograms can estimate individual patient outcomes after

75 The nomogram concordance indices were 0.68 (FFBF) and 0.74 (

76 ve accuracy of the constructed international nomogram (concordance index, 0.75) was significantly bet

77 A WIN nomogram, consisting of the combination of WIN and parat

78 However, the statistical foundations of nomogram construction, their precise interpretation, and

79 If validated in prospective cohorts, these nomograms could be used to predict seizure outcomes in p

80 The nomogram created in this model allows for the evaluation

81 One model included only the Kattan nomogram data; the other also incorporated imaging findi

82 The resulting nomogram demonstrated good accuracy in predicting MFS, w

83 study aims to propose a treatment-integrated nomogram derived from BCLC for patients with hepatocellu

84 cal approach to evaluating and comprehending nomogram-derived prognoses, with particular emphasis on

85 staging, allow for seamless incorporation of nomogram-derived prognosis to aid clinical decision maki

86 3, Irish and colleagues published a weighted nomogram designed to predict the risk of delayed graft f

87 The nomogram developed will be helpful to clinicians making

88 The average nomogram DGF risk was 0.41 (a 41% chance of DGF) among t

89 on of tyrosine kinase mutation status in the nomogram did not improve its discriminatory ability.

90 Risk stratification with a derived SPECT nomogram did not result in statistically significant net

91 The need for nomograms disappeared with the advent of personal comput

92 riate analysis, applying factors used in the nomogram, DSS of Korean GC patients remained significant

93 rating these estimates facilitate the use of nomograms during clinical encounters to inform clinical

94 c, and treatment variables were built into a nomogram estimating probability of IBTR at 5 and 10 year

95 Various nomograms exist today for identifying individuals at hig

96 A variety of predictive nomograms exists to predict lymph node involvement.

97 The nomograms facilitate prediction of outcomes following dr

98 A nomogram for 6-month mortality was developed and validat

99 A 12-year DSS postoperative nomogram for all sarcomas has already been established.

100 A nomogram for extremity STS that includes age, size, marg

101 research is required to develop a new valid nomogram for laser-assisted lens surgery.

102 A nomogram for mortality was created and tested on the rem

103 We developed the nomogram for overall survival using a Cox multivariable

104 urpose of this study was to build a specific nomogram for predicting postoperative overall survival (

105 igate new prognostic factors and construct a nomogram for predicting survival in individual patients.

106 On the basis of the multivariate analysis, a nomogram for predicting the 3- and 5-year risk of LR was

107 clinical parameters were used to construct a nomogram for predicting the risk of pN1.

108 The nomogram for prediction of 5- and 10-year IBTR probabili

109 The DCIS nomogram for prediction of 5- and 10-year IBTR probabili

110 f the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 pa

111 A nomogram for rituximab dose needed to obtain optimal AUC

112 Prognostic factors were used to develop nomograms for 2-year PFS, 5-year OS, and pelvic recurren

113 Nomograms for 2-year PFS, five-year OS, and pelvic recur

114 nd long-term seizure outcomes and to produce nomograms for estimation of individualised outcomes.

115 Methods To develop and validate nomograms for OS and PFS, we used a derivation cohort of

116 velop and externally validate two prediction nomograms for overall survival and distant metastases in

117 Age-based correction nomograms for presbyopia should therefore consider these

118 Clinical nomograms for the prediction of insignificant disease pr

119 A calculator (nomogram) for 90-day mortality was developed and validat

120 aim of our study was to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center to

121 ional hazards model was used to generate the nomogram from tumor burden, cirrhosis, performance statu

122 The nomogram had a concordance probability of 0.78 (SE 0.02)

123 PFS, OS, and pelvic recurrence nomograms had bootstrap-corrected concordance indices of

124 Conclusion A validated clinical nomogram has been developed to quantify the risk of earl

125 alvage radiotherapy, the first comprehensive nomogram has been developed.

126 The obsolete calculators known as nomograms have become epidemic in recent medical literat

127 Clinical nomograms have been designed and validated for predictio

128 ons of obstruction have evolved and improved nomograms have been developed to define study population

129 Although weight-based nomograms have improved the efficacy and safety of dosin

130 This nomogram, however, was developed using population data f

131 d datasets to create and validate a modified nomogram, IBTR! version 2.0.

132 The Recurrence of Kidney Stone nomogram identifies kidney stone formers at greatest ris

133 A nomogram-illustrated model was derived on the basis of v

134 though both the Duke treadmill score and our nomogram-illustrated model were significantly associated

135 standard weight-based unfractionated heparin nomogram in ST-segment elevation myocardial infarction,

136 We then tested the nomograms in an external validation cohort operated on o

137 A postoperative nomogram including only size, site, and age predicts loc

138 The goal of this study was to construct a nomogram incorporating SLN metastasis size to accurately

139 The discriminatory ability of the nomogram indicates that this population should not be co

140 We also introduce a novel Bayesian nomogram indicating the amount of evidence that each fea

141 The DCIS nomogram integrates 10 clinicopathologic variables to pr

142 This nomogram integrates commonly available factors into a us

143 We validated the nomogram internally using a bootstrap procedure and subs

144 A nomogram introduced this new variable to the model.

145 pular in medicine from about 1925 to 1975, a nomogram is a crude graphical means for solving an equat

146 This nomogram is a predictive tool, upon external validation,

147 This nomogram is a useful tool that helps physicians and pati

148 on clinicopathologic factors, the recurrence nomogram is better able to account for tumor and patient

149 sk patients within any particular stage, the nomogram is expected to aid in treatment planning and fu

150 A nomogram is provided for survival probabilities 1 to 4 y

151 In addition, our easy-to-use nomogram may be able to identify potential LTS among pat

152 iverse prognostic and determinant variables, nomograms meet our desire for biologically and clinicall

153 We assessed nomogram model performance by examining overall accuracy

154 Recently, nomogram models have been designed that incorporate MRI

155 iginal BCLC system, the treatment-integrated nomogram of BCLC system had larger linear trend and like

156 Compared to the nomogram of original BCLC system, the treatment-integrat

157 a previously published, multi-institutional nomogram of outcomes for salvage radiotherapy (SRT) foll

158 The nomogram of the Barcelona Clinic Liver Cancer (BCLC) has

159 Published nomograms of pediatric echocardiographic measurements ar

160 l timing of S2P was determined by generating nomograms of risk-adjusted, 3-year, post-Norwood, TFS ve

161 computing features both accuracy and speed; nomograms offer only the latter.

162 Prospective application of a nomogram on a case-by-case basis did not contribute mean

163 This has led to the appearance of many nomograms on the internet and in medical journals, and a

164 Two weighted prognostic models or nomograms, one including and one excluding treatment reg

165 ictions, easy to calculate in the frame of a nomogram or of a transfusion score, can be used to ident

166 egimens through either evidence-based dosing nomograms or preferably through the use of dosing softwa

167 the same time period matched by a prognostic nomogram, patients with colloid carcinoma had a signific

168 ng a postresection pancreatic adenocarcinoma nomogram, patients with either tubular or colloid carcin

169 Based on our nomogram, patients with the most favorable characteristi

170 The colon cancer recurrence nomogram predicted relapse with a concordance index of 0

171 The STS nomogram predicted the local recurrence rate with a C-in

172 Two internally validated nomograms predicted 10- and 15-y PCa-specific survival p

173 ire cohort and for four groups predefined by nomogram-predicted risks: group 1: less than 3%; group 2

174 ve developed an international bladder cancer nomogram predicting recurrence risk after radical cystec

175 A nomogram predicting the 15-year risk of PCSM was develop

176 ith LTS was developed and used to generate a nomogram predicting the likelihood of surviving at least

177 tent surgery for ACC were selected to create nomograms predicting RFS and OS.

178 e who have not) and calibration (accuracy of nomogram prediction) when applied to the validation coho

179 Nomogram predictions of RFS seemed better calibrated tha

180 Nomogram predictions were well calibrated.

181 A weighted prognostic model, or nomogram, predictive for overall survival was constructe

182 The WIN nomogram predicts the likelihood of additional hyperfunc

183 Decision rules, such as nomograms, provide evidence-based and at the same time i

184 ically significant variables in a predictive nomogram provided a reliable point system for estimating

185 Conclusion The validated nomograms provided useful prediction of OS and PFS for p

186 etter multivariate risk assessment tools and nomograms providing continuous scales and incorporating

187 In conclusions, our developed nomogram resulted in more accurate individualized predic

188 iscriminative ability and calibration of the nomograms revealed good predictive ability as indicated

189 56% between the first and fifth quintiles of nomogram score.

190 independent of prostate-specific antigen or nomogram score.

191 e significantly different across tertiles of nomogram scores (log-rank P = .003;< .001).

192 For 10 of 10 evaluable surgeons, the median nomogram scores in the SLN+/no ALND group were <or=10.5.

193 ootstrap procedure and subsequently used the nomogram scores to further interpret the effects of adju

194 re in detail the clinicopathologic features, nomogram scores, and rates of axillary LR between groups

195 The nomogram showed an area under the receiver operating cha

196 cripts in PCa with Gleason's grading or with nomogram significantly improves the prediction rate of P

197 The nomogram still overestimates risk in a minority of patie

198 ctive analysis of survival using a validated nomogram suggested that survival was prolonged with pert

199 , we incorporated SLN metastasis size into a nomogram that accurately predicts the likelihood of havi

200 riables from an internationally validated GC nomogram that estimates the probability of 5- and 9-year

201 a minimum pathology dataset and a prognostic nomogram that may have utility in stratifying patients f

202 This novel nomogram that predicts postoperative mortality may facil

203 A multivariate nomogram that predicts the likelihood of residual axilla

204 nce guide for physicians to locate published nomograms that apply to the clinical decision in questio

205 than any diagnostic test alone; furthermore, nomograms that incorporate MRI or MRI/magnetic resonance

206 Nomograms that incorporate these independent predictors

207 For the overall survival nomogram, the variables selected applying a backward pro

208 tive technology in their practice, such as a nomogram, there is always a question of whether the new

209 Surgeons can use this prognostic nomogram to accurately provide patients with their 2-yea

210 We derived a transmission nomogram to determine the plausibility of direct or indi

211 Here, we constructed a prognostic nomogram to enable individualized predictions of surviva

212 etastatic colon cancer was used to develop a nomogram to estimate recurrence after curative surgery.

213 se 5 parameters allowed the compilation of a nomogram to estimate the individual risk of lymph node m

214 The reduced model was used to build a nomogram to estimate the risk of death in individual pat

215 We developed and validated a prognostic nomogram to guide shared decision making for these patie

216 Of 124 patients predicted by the Chun nomogram to have an upgrading event, 47 actually did.

217 ors of survival and were used to construct a nomogram to predict 12-year overall survival.

218 A nomogram to predict brain metastasis was constructed and

219 We aimed to develop a nomogram to predict RFS after surgery in the absence of

220 A nomogram to predict RFS based on tumour size (cm), locat

221 Accordingly, we developed a nomogram to predict the likelihood of long-term breast p

222 We developed a nomogram to predict the probability of cancer control at

223 A nomogram to predict the probability of death within 24 m

224 A nomogram to predict the probability of having four or mo

225 Our goal was to develop a nomogram to predict the risk of cycle-one SDRT to better

226 We, therefore, constructed a nomogram to predict the risk of pN1 prior to surgical re

227 th localized RCC and develop a comprehensive nomogram to quantitate survival differences.

228 We constructed nomograms to assist physicians in adjuvant therapy decis

229 We developed evidence-based nomograms to assist with clinical decision making.

230 proach for building, interpreting, and using nomograms to estimate cancer prognosis or other health o

231 We developed nomograms to predict complete freedom from seizures and

232 into multivariate risk assessment tools and nomograms to predict disease behavior and guide manageme

233 Nomograms to predict individual 30-day risk of complicat

234 We have constructed nomograms to predict individual risk of 30-day morbidity

235 Nomograms to predict RFS and OS after surgical resection

236 iscriminative ability and calibration of the nomograms to predict RFS and OS were tested using C stat

237 More recently, authors have created nomograms to predict stone-free outcome after ESWL.

238 nal validation was performed by applying the nomograms to the patients of an external cohort.

239 and in medical journals, and an increase in nomogram use by patients and physicians alike.

240 tion of the inherent uncertainties regarding nomogram use.

241 We derived a nomogram using mortality predictors derived from a logis

242 We constructed nomograms utilizing clinically and statistically signifi

243 When applied to an external cohort, the nomogram was accurate and discriminating with an AUC = 0

244 The nomogram was accurate and discriminating, with an area u

245 The nomogram was assessed by calculating concordance probabi

246 antly associated with death (P < 0.001), the nomogram was better at discrimination (concordance index

247 Using this score, a nomogram was built enabling individualized prediction of

248 A Web browser-based nomogram was built from this model to make individualize

249 model was used to predict HCC, after which a nomogram was computed to assess individualized risk.

250 Using these variables, a nomogram was constructed and subsequently validated usin

251 A nomogram was created for pN1 using these clinical parame

252 A nomogram was created from the logistic regression model.

253 A nomogram was created to easily predict the risk of anast

254 On the basis of these factors, a nomogram was created to predict survival of ICC after re

255 A nomogram was created using these variables (AUC = 0.80;

256 A recently developed nomogram was demonstrated to predict disease recurrence

257 A computerized version of the nomogram was developed and is available on the Memorial

258 A nomogram was developed as a graphical representation of

259 A nomogram was developed from a multivariable model based

260 The nomogram was developed from August 1, 2006, through Dece

261 A nomogram was developed on the basis of these five variab

262 A nomogram was developed to predict 5- and 10-year MFS, gi

263 asis of an Eastern and Western experience, a nomogram was developed to predict overall survival after

264 A predictive nomogram was developed with the linear predictor method

265 te chance of cure for individual patients, a nomogram was developed, which allowed for weighting of t

266 f the Memorial Sloan Kettering Cancer Center nomogram was higher in the ePLND+SNB than in the ePLND g

267 The resultant nomogram was internally validated and had a concordance

268 The nomogram was internally validated using bootstrapping an

269 Internal and external validation of the nomogram was performed to assess clinical utility.

270 A modified Fagan nomogram was provided to assist calculation of posttest

271 The nomogram was tested in patients from the Spanish Group f

272 A modified nomogram was then tested against 664 patients from Massa

273 However, the nomogram was underestimating the probability of BCR-free

274 The nomogram was verified for discrimination and calibration

275 The nomogram was well calibrated and had good discriminative

276 The nomogram was well calibrated, with no significant differ

277 A final model, or nomogram, was chosen based on both clinical and statisti

278 By using a nomogram we are trying to improve on the current practic

279 The nomogram we developed predicts the outcome of SRT and sh

280 Factors included in the nomogram were age (</= 50 vs. >50), size (</= 5 vs. >5 c

281 Clinical data required by the Chun nomogram were available from 201 patients from the Coope

282 Concordance probabilities of the nomogram were better than those of the two NIH staging s

283 multivariate model for 30-day mortality and nomogram were created.

284 e original and new treatment-integrated BCLC nomogram were used to evaluate the prognostic performanc

285 The nomograms were able to stratify patients into prognostic

286 Nomograms were constructed to predict each patient's ris

287 Nomograms were created from Cox models and internally va

288 Nomograms were developed to facilitate point-of-care ris

289 Nomograms were developed to predict DSD, LR, and DR.

290 Nomograms were externally validated in a cohort of 153 p

291 es (DM) rates were estimated, and predictive nomograms were generated.

292 Variables included in the nomograms were sex, seizure frequency, secondary seizure

293 sess risk factors and to create a recurrence nomogram, which was validated using an international, mu

294 a patients can be estimated by this clinical nomogram, which will allow the identification of patient

295 are needed, and recently verified prognostic nomograms will be discussed.

296 logy/American Heart Association weight-based nomogram with centrally monitored activated partial thro

297 Age, site, and size were used to construct a nomogram with concordance index of 0.703 in internal val

298 Purpose To construct a nomogram with the albumin-bilirubin (ALBI) grade to asse

299 INTERPRETATION: We present evidence-based nomograms with robust performance across populations of

300 A prognostic nomogram yielded success probability of catheter drainag