ライフサイエンスコーパス: clinical decision

1 ases, agreement was 100% with respect to the clinical decision.

2 the final cell counts are commonly used for clinical decisions.

3 mation regarding CKD progression may improve clinical decisions.

4 an important goal, especially for policy or clinical decisions.

5 o inform many commercial, administrative and clinical decisions.

6 uggesting that it could be useful in guiding clinical decisions.

7 oom et al. to illustrate how research guides clinical decisions.

8 concern for public health and for informing clinical decisions.

9 as the highest level of evidence to support clinical decisions.

10 e media, and this may translate to important clinical decisions.

11 lecular signatures, diagnostic features, and clinical decisions.

12 alities are constantly evolving to assist in clinical decisions.

13 to receive results in time to make informed clinical decisions.

14 associated with remission or flare to inform clinical decisions.

15 disease for accurate diagnosis and to inform clinical decisions.

16 an result in severely biased results and bad clinical decisions.

17 asurement of body temperature will influence clinical decisions.

18 der how the immunobiology of IL-6 may inform clinical decisions.

19 on, which is important if it helps in making clinical decisions.

20 ical Examination may be helpful in assisting clinical decisions.

21 s how to integrate scientific knowledge into clinical decisions.

22 atients worldwide stymies basic research and clinical decisions.

23 t provide accurate molecular data in guiding clinical decisions.

24 t concern for ASD should not factor into the clinical decision about whether to induce labor.

25 or stage at diagnosis is a pivotal point for clinical decisions about patient treatment strategies.

26 friendly, mobile (iPad, iPhone, and website) clinical decision aid called iChoose Kidney.

27 The use of a mobile clinical decision aid comparing individualized mortality

28 Using these data, we developed a mobile clinical decision aid that provides estimates of risks o

29 specially in conjunction with well-validated clinical decision algorithms, now enables the clinician

30 activity, and fibrosis score, can help guide clinical decisions and assess outcomes of clinical trial

31 are, and to generate new knowledge to inform clinical decisions and national policies.

32 in those with MCI are required to guide both clinical decisions and public health policy, but publish

33 by identifying levels that may be useful in clinical decisions, and evaluated its utility for predic

34 ng HIV disease progression, making important clinical decisions, and monitoring the response to antir

35 impact on response to ibrutinib, may inform clinical decisions, and should be evaluated in larger da

36 arketing messages at the point of care, when clinical decisions are being made.

37 e premise of evidence-based medicine is that clinical decisions are informed by the peer-reviewed lit

38 ing diagnostic accuracy information to guide clinical decisions are not systematically associated wit

39 cians can consider using this tool to inform clinical decisions as further studies are done to determ

40 dictors of lead failure that can assist with clinical decisions as to whether lead revision should be

41 scientific literature to make evidence-based clinical decisions based on molecular profiling results

42 t the treating physician can prioritize what clinical decisions can be pursued in order to provide ca

43 ame test measured at screening and baseline, clinical decisions could be impacted in 14.4% and 6.2% o

44 These data could inform clinical decisions for patients at high risk of fracture

45 These data could help inform clinical decisions for patients with non-small-cell lung

46 tive outcomes can inform future research and clinical decisions for patients with TBI.

47 and other data to make individually tailored clinical decisions for patients, although the path to ac

48 d medicine within reach, directly supporting clinical decisions for the selection of treatment option

49 re facing increasingly difficult ethical and clinical decisions given the rapidly expanding aging dem

50 g treatment is well described for individual clinical decisions; however, its role in evaluations of

51 Patients undergoing thoracoscopy (n = 206; clinical decision if biopsy was required) received a 24F

52 e antiviral T cell immunity is important for clinical decisions in immunodeficient patients.

53 VC is established, the single most important clinical decision is whether a particular patient's sudd

54 an evidence base was developed to inform the clinical decisions made around antibiotic treatment over

55 dents to effectively challenge clearly wrong clinical decisions made by their staff.

56 nd radiographic or ultrasonography changes); clinical decision making (additional testing and pharmac

57 iases were not significantly associated with clinical decision making among acute care surgical clini

58 This finding supports clinical decision making and application of biomarkers i

59 is a user-friendly tool that may facilitate clinical decision making and appropriate stratification

60 horacic MR imaging substantially affects the clinical decision making and diagnostic certainty of tho

61 racic magnetic resonance (MR) imaging on the clinical decision making and diagnostic certainty of tho

62 gestions provided are intended to facilitate clinical decision making and encourage an evidence-based

63 generating actionable data that will inform clinical decision making and facilitate development of n

64 This observation has the potential to guide clinical decision making and further refine risk stratif

65 anation that will assist clinicians in their clinical decision making and interpretation of troponin

66 Clinical decision making and interprofessional communica

67 Clinical decision making and interprofessional communica

68 ck page cases as a valid construct to assess clinical decision making and interprofessional communica

69 ck page cases as a valid construct to assess clinical decision making and interprofessional communica

70 n occurring after IVTA provides guidance for clinical decision making and management of patients trea

71 y nonradiologist pediatricians can assist in clinical decision making and procedural success.

72 s appears likely, leading to better-informed clinical decision making and providing insight into dise

73 t compliance with medical therapy may inform clinical decision making and should be incorporated into

74 These results are important for clinical decision making and suggest that adenotonsillec

75 nical research are slow to have an impact on clinical decision making and thus to benefit patients; 2

76 tic classification, which can in turn inform clinical decision making and treatment matching.

77 Risk stratification is the cornerstone for clinical decision making and treatment selection for the

78 currence of each respective event and inform clinical decision making are lacking.

79 patients in the group in whom challenges in clinical decision making are most prevalent.

80 Non-clinical factors (NCFs) influence clinical decision making but are rarely considered.

81 This review article seeks to aid clinical decision making by providing an overview of ava

82 ular profiling data has been used to improve clinical decision making by stratifying subjects based o

83 However, most ICU clinical decision making continues to take place indepen

84 ion of presented results may also facilitate clinical decision making during surgery for large renal

85 gained traction as an important adjunct for clinical decision making during vitreoretinal surgery, a

86 incorporating cardiac magnetic resonance in clinical decision making for defibrillator therapy are w

87 many aspects and could aid in diagnosis and clinical decision making for patients with acute, acute

88 predictors and may have an ascendant role in clinical decision making for poststroke rehabilitation,

89 Such insight might be useful in the clinical decision making for those who apply emicizumab

90 al evaluation process, this tool can support clinical decision making for treatment duration.

91 Incorporating biomarkers into clinical decision making has the potential to more accur

92 e useful for genetic counseling and may help clinical decision making in a fast and cost-efficient ma

93 function plays an essential role for optimal clinical decision making in a variety of diseases.

94 R values derived from patients with SIHD for clinical decision making in ACS patients.

95 Although FFR is increasingly used for clinical decision making in acute coronary syndrome (ACS

96 aking, which is reflective of the reality of clinical decision making in acute hospital wards.

97 deep learning aided diagnosis can facilitate clinical decision making in breast cancer by identifying

98 PC3 mutations, there is little data to guide clinical decision making in cases with double mutations.

99 k stratification is an integral component of clinical decision making in heart failure (HF).

100 MTRs can and have been validated for use in clinical decision making in malignant diseases, along wi

101 ere is a pervasive lack of evidence to guide clinical decision making in older patients with cardiova

102 8)F-FET PET can add valuable information for clinical decision making in pediatric brain tumor patien

103 Future studies assessing the role of MRI in clinical decision making in terms of prognostic value fo

104 The model has the potential to facilitate clinical decision making in the staging of NSCLC.

105 day mortality rates should promote review of clinical decision making in these hospitals.

106 These data are potentially important for clinical decision making in this patient population.

107 oration of observational research as part of clinical decision making is consistent with the position

108 of key evidence-based medicine principles in clinical decision making is fundamental to preventing ov

109 yptococcal meningitis, but its use in aiding clinical decision making is hampered by the time involve

110 derstanding of how unconscious biases affect clinical decision making may help to illuminate clinicia

111 prognostically significant and could inform clinical decision making on primary prevention ICD candi

112 proved in general for both communication and clinical decision making over the 4-week course.

113 proved in general for both communication and clinical decision making over the 4-week course.

114 literature provide only limited guidance in clinical decision making owing to heterogeneity and scar

115 substitute incorporation is critical in the clinical decision making process and requires special in

116 r of bone graft incorporation and can aid in clinical decision making provided standard radiographic

117 bacterial infections, hopefully facilitating clinical decision making regarding further investigation

118 However, limited evidence exists to aid clinical decision making regarding which patients will b

119 Clinical decision making relative to community-acquired

120 ho diagnose and manage Kawasaki disease, but clinical decision making should be individualized to spe

121 a conceptual model of the process of shared clinical decision making that involves four stepped leve

122 nd treatment efficacy predictions for better clinical decision making through large volume of data.

123 Incorporation of biomarkers into clinical decision making to define therapeutic managemen

124 osis factor agents and thiopurines to inform clinical decision making when applying TDM in a reactive

125 The results of this study will aid clinical decision making when choosing biologic therapy

126 The results of this study should support clinical decision making when choosing second-line biolo

127 on is commonly used but infrequently impacts clinical decision making with major financial burden.

128 ery by nearly one-third and could help guide clinical decision making with regard to surveillance ver

129 rent ability of 'omic' information to inform clinical decision making, (4) emerging ideas about the t

130 Lyme disease, the C6 EIA could guide initial clinical decision making, although a supplemental immuno

131 G PET/CT may have a significant influence on clinical decision making, although its role is still evo

132 m development of new diagnostics, facilitate clinical decision making, and improve surveillance for d

133 iew the effects of RAS and BRAF mutations on clinical decision making, and reflect on future directio

134 al role of patient values and preferences in clinical decision making, and the development of the met

135 the condition limits the knowledge base for clinical decision making, but a few published randomised

136 difficult-to-control asthma is important for clinical decision making, drug development, and reimburs

137 onal status, with potential implications for clinical decision making, especially within prospective

138 In clinical decision making, in addition to anatomical info

139 and its potential power to facilitate better clinical decision making, particularly in the care of pa

140 not replicate; 4) when the results influence clinical decision making, the results clinicians obtain

141 apeutic approaches play an important role in clinical decision making, treatment guidelines, and heal

142 To assist clinical decision making, we pooled the existing evidenc

143 re units and its use has a notable effect on clinical decision making.

144 ility of SCA to have a significant effect on clinical decision making.

145 icting individual ECC outcomes and informing clinical decision making.

146 advance clinical research and better inform clinical decision making.

147 using natural language processing to enrich clinical decision making.

148 l confusion about how to use the results for clinical decision making.

149 iates in trials to emphasize their impact on clinical decision making.

150 ta confirm that FFR</=0.80 is valid to guide clinical decision making.

151 of 30-day mortality, and ultimately improve clinical decision making.

152 ue of 0.80 has been widely accepted to guide clinical decision making.

153 lysis of diagnostic studies that will aid in clinical decision making.

154 hting again the importance of genotype-based clinical decision making.

155 weight-loss surgery are available to support clinical decision making.

156 of the cardiovascular system are central for clinical decision making.

157 affect disease definitions or contribute to clinical decision making.

158 thostatic HRR predicts mortality and may aid clinical decision making.

159 ow comparisons across trials, and strengthen clinical decision making.

160 th patient values and preferences to improve clinical decision making.

161 nal development of novel immunotherapies and clinical decision making.

162 ity of the appropriate use criteria to guide clinical decision making.

163 he relationship between unconscious bias and clinical decision making.

164 oration of nomogram-derived prognosis to aid clinical decision making.

165 ration of long-term outcome information into clinical decision making.

166 fter an intervention as opposed to improving clinical decision making.

167 tic acute kidney injury could help to inform clinical decision making.

168 hresholds are necessary to facilitate better clinical decision making.

169 classification system that might be used in clinical decision making.

170 It is unknown whether this assay impacts clinical decision making.

171 ease and primary FSGS and may serve to guide clinical decision making.

172 se challenges for diagnosis, treatments, and clinical decision making.

173 are appropriate, and to provide guidance on clinical decision making.

174 would increase the information available for clinical decision making.

175 ieved, potentially providing assistance with clinical decision making.

176 These 2 predictors should influence clinical decision making.

177 radiation and temozolomide and to influence clinical decision making.

178 nclature for seizure risk stratification and clinical decision making.

179 ded before they can be used as biomarkers in clinical decision making.

180 y important since data provide the basis for clinical decision making.

181 t-term mortality and might, thus, facilitate clinical decision making.

182 nhance the role of observational research in clinical decision making: (1) improve the quality of ele

183 ver metastases would be invaluable to inform clinical decision making; however, deriving this informa

184 cts, nurses' autonomy, scope of practice and clinical decision-making abilities.

185 Both policy-level and clinical decision-making about LDCT screening must consi

186 for chronic viral hepatitis C (CHC), shared clinical decision-making addresses the need to engage pa

187 To perform a clinical decision-making analysis of Sepsis-3 in patient

188 CMML patients, providing a robust basis for clinical decision-making and a reliable tool for clinica

189 Therefore, prognostic tools to guide clinical decision-making and avoid overtreatment of indo

190 for disease progression would be useful for clinical decision-making and designing clinical trials.

191 Frailty and dementia should be considered in clinical decision-making and guideline development.

192 al pathogens prior to culturing could inform clinical decision-making and improve reaction time.

193 Genetics is already being used to direct clinical decision-making and its contribution is likely

194 ML have significantly improved our tools for clinical decision-making and promise to identify new the

195 f how to best incorporate genomic testing in clinical decision-making and subsequent treatment recomm

196 available molecular markers truly influence clinical decision-making and treatment.

197 These findings raise important questions for clinical decision-making and value-based policy.

198 me endpoints, absolute changes and impact on clinical decision-making are marginal.

199 improve prognostication and, more generally, clinical decision-making because the different driver mu

200 These risk/benefit data serve as a basis for clinical decision-making before entering an intraportal

201 h tools could improve treatment by informing clinical decision-making before the commencement of trea

202 and therefore have the potential to advance clinical decision-making by systematically analyzing sta

203 and treatment of breast cancer have made the clinical decision-making context much more complex.

204 add incremental diagnostic value but guides clinical decision-making in an unsalutary fashion.

205 application of ANNs as a tool for assisting clinical decision-making in neurosurgery.

206 ng can identify mutations that could improve clinical decision-making in routine cancer care, potenti

207 ibility testing remains a limiting factor in clinical decision-making in the treatment of bacterial i

208 However, clinical decision-making is confounded by the fact that

209 A key factor in clinical decision-making is that patients with mutations

210 ation could contribute to improving both the clinical decision-making process in and management of th

211 individual risk estimate contributes to the clinical decision-making process.

212 ntial complementary role in the acute stroke clinical decision-making process.

213 importance of SVT and may form the basis for clinical decision-making regarding anticoagulation.

214 r patient engagement, the development of new clinical decision-making support tools, and the validati

215 ) data are available, and they actively help clinical decision-making through the assessment of wheth

216 f hemodynamics may serve as a supplement for clinical decision-making to prevent the occurrence of a

217 To our knowledge, this is the first clinical decision-making tool that generates personalize

218 More accurate AKI risk estimates may improve clinical decision-making when attempting to balance the

219 Although clinical decision-making will be guided mainly by clinic

220 rate hemodynamic assessment is important for clinical decision-making, O2 should be directly measured

221 rediction tools are yet useful for practical clinical decision-making, probably reflecting our limite

222 aggressiveness with the potential to impact clinical decision-making, such as targeted biopsy approa

223 d scales offer a potentially useful tool for clinical decision-making, tailoring treatment to patient

224 of matched genomic-clinical data can support clinical decision-making.

225 y disorders, with potential implications for clinical decision-making.

226 ve disorder (MDD) illness course complicates clinical decision-making.

227 r system function after stroke could improve clinical decision-making.

228 SSI versus risk of AKI is needed to improve clinical decision-making.

229 come are not precise enough to guide initial clinical decision-making.

230 iple categories to reflect the complexity of clinical decision-making.

231 can provide objective information to support clinical decision-making.

232 Purpose To determine the demographic, clinical, decision-making, and quality-of-life factors t

233 ty of the information provided from them for clinical decision management.

234 well as the use of quantitative results for clinical decisions may warrant reconsideration.

235 critically appraise the quality of published clinical decision models and draw well founded conclusio

236 et with complex patient histories from which clinical decisions must be made.

237 The divergent findings suggest that clinical decisions on SSRI use during pregnancy should b

238 19 genotype status should not be included in clinical decisions on tamoxifen treatment.

239 intensive therapy, which might help to guide clinical decisions regarding the management of such pati

240 hysician characteristics that influence this clinical decision remains unclear.

241 isms may also be safely characterized by the clinical decision rule and spared radiation exposure.

242 We aimed to validate three clinical decision rules (PECARN, CATCH, and CHALICE) in

243 , involving both non-invasive bedside tools (clinical decision rules and D-dimer blood tests) for pat

244 needed in development of pediatric-specific clinical decision rules and risk stratification and in t

245 Clinical decision rules are able to predict intra-abdomi

246 Clinical decision rules can help to determine the need f

247 history and physical examination features in clinical decision rules can identify patients with minor

248 INTERPRETATION: The sensitivities of three clinical decision rules for head injuries in children we

249 substantially; however, the Grote and Saari clinical decision rules seem the most promising.

250 onditions and with internationally validated clinical decision rules to define abnormal growth, inclu

251 consisted of the sequential application of a clinical decision score, d-dimer testing, and ultrasonog

252 The combination of a clinical decision score, d-dimer testing, and ultrasonog

253 venous thrombosis, an algorithm combining a clinical decision score, d-dimer testing, and ultrasonog

254 nation with LA volumetry could help to guide clinical decisions, since myocardial structural remodeli

255 by echocardiography is influencing important clinical decisions, such as the decision to undergo MR s

256 after, provider overrides of evidence-based clinical decision support (CDS) for ordering computed to

257 dology and findings could be used to improve clinical decision support and personalize trajectories,

258 e management provided by trained nurses, and clinical decision support for PCPs by consulting physici

259 Computerized clinical decision support integrated with the electronic

260 clinics were randomized to the computerized clinical decision support intervention, aimed at physici

261 phenotype knowledge base, and development of clinical decision support software are needed in additio

262 We evaluated the effectiveness of a computer clinical decision support system (CDSS) for reducing the

263 A clinical decision support system (CDSS) is a computer pr

264 A clinical decision support system (CDSS) is a computer pr

265 Use of a clinical decision support system (CDSS) might improve ou

266 We developed an open-source, generalizable clinical decision support system called Electronic Suppo

267 open-source, electronic health record-based clinical decision support system can increase AE detecti

268 Use of a computerized clinical decision support system to automate the identif

269 Using a computerized clinical decision support system to automate the screeni

270 scribe the potential role for a computerized clinical decision support system to enable non-specialis

271 sibility and effectiveness of a computerized clinical decision support system to identify pediatric p

272 Computerized clinical decision support systems (CCDSSs) have been imp

273 Clinical decision support systems (CDSS) can scan the pa

274 Electronic health records (EHRs) and clinical decision support systems (CDSSs) have the poten

275 Clinical decision support systems (CDSSs) might be benef

276 Demand for clinical decision support systems in medicine and self-d

277 that reduce or prevent alert fatigue within clinical decision support systems.

278 ed to reduce or prevent alert fatigue within clinical decision support systems.

279 We implemented and studied the impact of a clinical decision support tool (CDST) to decrease the nu

280 alidation and evaluation in the context of a clinical decision support tool are warranted.

281 mechanics, difficulties, and gaps in genomic clinical decision support tool development.

282 e of supporting clinician order entry and of clinical decision support tools (CDSTs) has provided exp

283 ng of children with BMI >/= 85th percentile, clinical decision support tools for pediatric weight man

284 ved reporting practices of BCID results with clinical decision support tools providing interpretation

285 Use of clinical decision support tools to enable substitution o

286 Documentation tools, order sets, and clinical decision support were designed to improve effic

287 anding of critical illness, enable real-time clinical decision support, and improve both clinical out

288 parameters-which are the basis of day-to-day clinical decision support-are often used to guide the cl

289 inical studies and database development, and clinical decision support.

290 ue Web site views; 112 users (38%) completed clinical decision support.

291 nts after PCI with DES, thereby facilitating clinical decisions surrounding the optimal duration of D

292 a machine learning framework for augmenting clinical decision systems.

293 ents with ECOG performance status 0 to 2 and clinical decision to treat with bisphosphonates within 3

294 most appropriate in primary prevention when clinical decisions to initiate statin therapy are uncert

295 cNairy and colleagues highlight the need for clinical decision tools to help identify HIV patients wh

296 Our findings suggest that a clinical decision tree can be used to estimate a bactere

297 With simple variables, clinical decision trees can be used to distinguish patie

298 Clinical decision trees were developed using the recursi

299 m patients and may result from inappropriate clinical decision values (CDVs) for cardiac troponin (cT

300 often used interchangeably to make critical clinical decisions, yet few studies have compared these