ライフサイエンスコーパス: clinical decision making

1 ients when these markers are integrated into clinical decision making.

2 ide database for epidemiological studies and clinical decision making.

3 the established biomarkers that are used for clinical decision making.

4 heir use in doctor-patient communication and clinical decision making.

5 owever, there is limited evidence to support clinical decision making.

6 sly reported, and potentially influential in clinical decision making.

7 t incorporate the concept of sarcopenia into clinical decision making.

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

9 radiation and temozolomide and to influence clinical decision making.

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

11 lesions on root surfaces in order to improve clinical decision making.

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

13 would increase the information available for clinical decision making.

14 ieved, potentially providing assistance with clinical decision making.

15 ary biomarkers that may be used for specific clinical decision making.

16 These 2 predictors should influence clinical decision making.

17 nclature for seizure risk stratification and clinical decision making.

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

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

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

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

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

23 eatment effects, the most relevant scale for clinical decision making.

24 icting individual ECC outcomes and informing clinical decision making.

25 advance clinical research and better inform clinical decision making.

26 using natural language processing to enrich clinical decision making.

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

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

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

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

31 predicting treatment outcomes and informing clinical decision making.

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

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

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

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

36 affect disease definitions or contribute to clinical decision making.

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

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

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

40 nal development of novel immunotherapies and clinical decision making.

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

42 he relationship between unconscious bias and clinical decision making.

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

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

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

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

47 hresholds are necessary to facilitate better clinical decision making.

48 had low-level mutations somehow relevant for clinical decision making.

49 and has the potential to be integrated into clinical decision making.

50 ation, and it can be used as a tool to guide clinical decision making.

51 roaches to capture and account for it during clinical decision making.

52 de a basis for more informed counselling and clinical decision making.

53 prevention clinical trials and personalized clinical decision making.

54 e-clinical models and the potential to guide clinical decision making.

55 ide database for epidemiological studies and clinical decision making.

56 ss or mortality is essential for appropriate clinical decision making.

57 ent portion of risk to be routinely used for clinical decision making.

58 h it, and patients are often not involved in clinical decision making.

59 ings need to be confirmed before influencing clinical decision making.

60 -related information and could be helpful in clinical decision making.

61 ng of apoptosis post-therapy could assist in clinical decision making.

62 ents having undergone pre-PCI FFR as part of clinical decision making.

63 n psoriatic arthritis are helpful in guiding clinical decision making.

64 as used to assess how PSQ results influenced clinical decision making.

65 of clinical decision support to assist with clinical decision making.

66 mes in CS and whether a risk score can guide clinical decision making.

67 tree from the ensemble that can be used for clinical decision-making.

68 ist early and accurate diagnosis and improve clinical decision-making.

69 utility of ML techniques to support informed clinical decision-making.

70 he EHR based on systemic data to assist with clinical decision-making.

71 iculous surgery, endoscopic surveillance and clinical decision-making.

72 o automated medical diagnoses that can guide clinical decision-making.

73 tter inform trial stratification and improve clinical decision-making.

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

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

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

77 l care and to support patient involvement in clinical decision-making.

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

79 of viremia are urgently needed to accelerate clinical decision-making.

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

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

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

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

84 MDS/MPN subtypes, which may be relevant for clinical decision-making.

85 uld provide fine-grained resolution to guide clinical decision-making.

86 of stillbirth risk has potential to support clinical decision-making.

87 erred method to generate evidence to support clinical decision-making.

88 , and generalizable performance for enhanced clinical decision-making.

89 ing personalized treatments and facilitating clinical decision-making.

90 tial prognostic and predictive potential for clinical decision-making.

91 herefore impact FFR measurements and related clinical decision-making.

92 cell carcinoma aggressiveness may help guide clinical decision-making.

93 re uncommon and therefore unlikely to affect clinical decision-making.

94 ring radiographic workup and integrated into clinical decision-making.

95 hould lead to better risk stratification and clinical decision-making.

96 s to develop tools that can be used to guide clinical decision-making.

97 or aiding in the efficiency and precision of clinical decision-making.

98 -related information and could be helpful in clinical decision-making.

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

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

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

102 the disease processes will facilitate better clinical decision-making about the therapies offered, ex

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

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

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

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

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

108 This observation might guide clinical decision making among providers treating immune

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

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

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

112 za with RT-PCR; results were unavailable for clinical decision making and clinical influenza testing

113 These data could support clinical decision making and could also serve as outcome

114 ession, and therefore have limited value for clinical decision making and development of novel therap

115 Machine learning promises to revolutionize clinical decision making and diagnosis.

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

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

118 Payments may influence physicians' clinical decision making and drug prescribing.

119 ciated with high test variability, impacting clinical decision making and efficiency.

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

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

122 d intensity of risk factor interventions for clinical decision making and for guideline-directed care

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

124 lans from a 3D scan alone, to help efficient clinical decision making and improve clinical understand

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

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

127 Clinical decision making and interprofessional communica

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

129 gher DTA could have negative consequences on clinical decision making and patient care.

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

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

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

133 These data may inform clinical decision making and should be the basis for fut

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

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

136 liver novel EHR interventions that influence clinical decision making and to support efficient random

137 onary atherosclerosis to guide physicians in clinical decision making and treatment of athletes with

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

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

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

141 Current clinical decision-making and care pathways must be furth

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

143 This can assist clinical decision-making and enable better pre-operative

144 S-CoV-2) serologic assays is needed to guide clinical decision-making and ensure that these assays pr

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

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

147 value, 2 - information gained did not impact clinical decision-making and in case of a therapeutic in

148 mation gained was essential and critical for clinical decision-making and in case of a therapeutic in

149 ent, 3 - information gained had an impact on clinical decision-making and in the case of a therapeuti

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

151 r End-Stage Liver Disease (MELD) is used for clinical decision-making and organ allocation for orthot

152 indicators of organ dysfunction may improve clinical decision-making and outcome of patients.

153 termine the impact of patient preferences on clinical decision-making and outcomes.

154 ascertain prognostic indicators that inform clinical decision-making and practices regarding the rol

155 vivo-derived measurements and could support clinical decision-making and provide surrogate end point

156 ive risk prediction is important for guiding clinical decision-making and resource allocation.

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

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

159 l-rich tertiary lymphoid structures to guide clinical decision-making and treatments, which could hav

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

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

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

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

164 om randomized controlled trials exist to aid clinical decision-making, and the findings from observat

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

166 lly significant and therefore able to impact clinical decision-making; and (3) whether DeltaFFR(eng)-

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

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

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

170 nd subsequent graft function is important in clinical decision-making around kidney transplantation,

171 nical trials in T-PLL, and will thus support clinical decision making, as well as the approval of new

172 help the triaging of TBI patients and assist clinical decision making at point-of-care settings.

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

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

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

176 bility of this model may prove beneficial in clinical decision-making both prior to and following tra

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

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

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

180 od may present a promising avenue to support clinical decision making by providing empirical informat

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

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

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

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

185 Optimal clinical decision-making depends on identification of cl

186 VD and have even increased mortality, making clinical decision making difficult.

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

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

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

190 ype of approach has the potential to improve clinical decision making for common and rare diseases.Su

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

192 hors of this commentary provide guidance for clinical decision making for patients with coronavirus d

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

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

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

196 data may be used to quantify risk and guide clinical decision-making for all phenotypes of CS.

197 Timely and effective clinical decision-making for COVID-19 requires rapid ide

198 been identified, and their integration into clinical decision-making for patients with advanced-stag

199 about the state of fracture repair to guide clinical decision-making for patients.

200 RHR should be integrated into comprehensive clinical decision-making for these patients.

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

202 e find that good quality AI-based support of clinical decision-making improves diagnostic accuracy ov

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

204 as novel biomarkers for HCM facilitating the clinical decision making in a personalized manner.

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

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

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

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

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

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

211 reactivation should prove useful in guiding clinical decision making in HCT recipients.

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

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

214 ells.Sig could represent a valuable tool for clinical decision making in patients receiving immunothe

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

216 mechanisms of action, which could help guide clinical decision making in the management of patients w

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

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

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

220 The role of troponin testing to assist clinical decision making in this setting is unexplored.

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

222 scenarios, which can be used as a guide for clinical decision-making in daily practice.

223 ures will increasingly be crucial to guiding clinical decision-making in each patient with cancer.

224 Clinical decision-making in kidney transplant (KT) durin

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

226 vides valuable frameworks and benchmarks for clinical decision-making in patient management, improved

227 in randomized clinical trials and may guide clinical decision-making in patients who experience earl

228 ntial of deep learning to assist and enhance clinical decision-making in patients with AMD, such as e

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

230 failure is warranted for prognostication and clinical decision-making in the post-cardiac arrest peri

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

232 In clinical decision making, in addition to anatomical info

233 the point-of-care, and could help to improve clinical decision making, infection control, and epidemi

234 pairments after critical illness could guide clinical decision-making, inform trial enrollment, and f

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

236 Clinical decision making is extremely difficult in this

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

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

239 inimal risk or quality improvement, and when clinical decision making is supported, rather than contr

240 n evidence base that is not aligned with how clinical decision-making is actually performed.

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

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

243 s appraise the patient data set that informs clinical decision-making is unknown.

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

245 Understanding these risk factors during clinical decision-making may improve prevention of DGF a

246 al and translational applications, including clinical decision making, medical diagnosis, drug repurp

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

248 SOPCP did not affect clinical decision-making or alter clinical course (grade

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

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

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

252 f peri-implant stability or disease to guide clinical decision-making post-treatment.

253 roducing processes that facilitate rationale clinical decision making, predictive or prognostic model

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

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

256 , advocating for its implementation into the clinical decision-making process besides usual clinical

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

258 ld be part of routine DMR evaluation and the clinical decision-making process.

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

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

261 factors, alongside qualitative research into clinical decision-making processes and patients' experie

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

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

264 facilitate perioperative planning and inform clinical decision making regarding post-operative rhythm

265 This can help inform clinical decision making regarding the need for a right

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

267 These findings facilitate clinical decision-making regarding allergic diseases in

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

269 Clinical decision-making regarding the optimization of t

270 on and optimization must remain the basis of clinical decision-making regarding the use of ionizing r

271 Little is known about real-world clinical decision-making related to hospice for members

272 Clinical decision making relative to community-acquired

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

274 Clinical decision making should not be made based on a v

275 that uses a key features approach to measure clinical decision-making skills and focuses on cases enc

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

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

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

279 simultaneously, to support more personalized clinical decision making than can be made on the basis o

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

281 sk factors can guide appropriate consent and clinical decision-making that may reduce anastomotic-rel

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

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

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

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

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

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

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

289 riants in these genes should not be used for clinical decision-making, unless accompanied by new and

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

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

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

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

294 ving a comprehensive reference to help guide clinical decision making when treating patients.

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

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

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

298 nt a surgical treatment algorithm to support clinical decision making, with the aim to encourage tran

299 ng can provide information of great value in clinical decision-making, yet RNA from readily available

300 ppropriate Use Criteria were designed to aid clinical decision-making, yet their association with hea