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1 ine clinical factors, are likely to affect a clinical decision.
2 ing statistical significance), and/or advise clinical decisions.
3  disease course and can inform postoperative clinical decisions.
4 onsider out-of-pocket costs when making most clinical decisions.
5 ble to predict 7-day mortality and may guide clinical decisions.
6 ), require several days before informing key clinical decisions.
7 h-risk patients who may benefit from earlier clinical decisions.
8 st drug repositioning, altogether supporting clinical decisions.
9 herapies in patients with IBS to help inform clinical decisions.
10 lation of analytical information into future clinical decisions.
11  when deciding how to best use them to guide clinical decisions.
12 , which directly translates into significant clinical decisions.
13  the final cell counts are commonly used for clinical decisions.
14 mation regarding CKD progression may improve clinical decisions.
15 e media, and this may translate to important clinical decisions.
16 disease for accurate diagnosis and to inform clinical decisions.
17 ical Examination may be helpful in assisting clinical decisions.
18 s how to integrate scientific knowledge into clinical decisions.
19 atients worldwide stymies basic research and clinical decisions.
20 ven, algorithm-based biomedical research and clinical decisions.
21 t provide accurate molecular data in guiding clinical decisions.
22 tratification that is the foundation of many clinical decisions.
23 f these components of variability in forming clinical decisions.
24  interventions by efficacy, to better inform clinical decisions.
25 ibility through smartphones helpful to guide clinical decisions.
26 G12, will influence research and potentially clinical decisions.
27 f the available data were assessed to inform clinical decisions about non-invasive neuromodulation.
28 tools with concrete questions about specific clinical decisions aimed at reducing suicides and to eva
29 CR-ABL1 KD mutation screening results in the clinical decision algorithms.
30                       Purpose To construct a clinical decision analysis model for assessing intraoper
31 erging as a promising approach to facilitate clinical decisions and improve patient stratification.
32  is therefore of high importance for ensuing clinical decisions and overall success of allogeneic ste
33 in those with MCI are required to guide both clinical decisions and public health policy, but publish
34  by identifying levels that may be useful in clinical decisions, and evaluated its utility for predic
35  impact on response to ibrutinib, may inform clinical decisions, and should be evaluated in larger da
36                                              Clinical decisions are ideally based on evidence generat
37 ing diagnostic accuracy information to guide clinical decisions are not systematically associated wit
38 cians can consider using this tool to inform clinical decisions as further studies are done to determ
39 m the public health policies, and can inform clinical decisions as well.
40 scientific literature to make evidence-based clinical decisions based on molecular profiling results
41                                              Clinical decisions based on sagittal translations of les
42 ohort and compared the relative utilities of clinical decisions based on these tools to existing stra
43 t the treating physician can prioritize what clinical decisions can be pursued in order to provide ca
44 rapid multiplex PCR with provider education, clinical decision-care algorithms, and active antibiotic
45                   However, several important clinical decisions depend on whether renal dysfunction i
46 reasingly applied to biomedical research and clinical decisions, developing unbiased AI models that w
47                      These data could inform clinical decisions for patients at high risk of fracture
48 and other data to make individually tailored clinical decisions for patients, although the path to ac
49 e new insights that might guide personalized clinical decisions for PTEN-variant carriers.
50 quencing and/or ALT analysis may help in the clinical decisions for these small PanNETs.
51                       We sought to develop a clinical decision guideline (CDG) to inform influenza te
52  2016 and 2020, aiming to demystify CAC as a clinical decision-guiding tool and push the limits of wh
53 g treatment is well described for individual clinical decisions; however, its role in evaluations of
54  potential to risk stratify patients to make clinical decisions, including timing for surgical treatm
55 ics of one or more assays with predetermined clinical decision limits and may help improve the develo
56 dents to effectively challenge clearly wrong clinical decisions made by their staff.
57                 This observation might guide clinical decision making among providers treating immune
58                        This finding supports clinical decision making and application of biomarkers i
59 za with RT-PCR; results were unavailable for clinical decision making and clinical influenza testing
60                     These data could support clinical decision making and could also serve as outcome
61 ession, and therefore have limited value for clinical decision making and development of novel therap
62   Machine learning promises to revolutionize clinical decision making and diagnosis.
63           Payments may influence physicians' clinical decision making and drug prescribing.
64 ciated with high test variability, impacting clinical decision making and efficiency.
65 gestions provided are intended to facilitate clinical decision making and encourage an evidence-based
66 d intensity of risk factor interventions for clinical decision making and for guideline-directed care
67 lans from a 3D scan alone, to help efficient clinical decision making and improve clinical understand
68 ck page cases as a valid construct to assess clinical decision making and interprofessional communica
69 gher DTA could have negative consequences on clinical decision making and patient care.
70 s appears likely, leading to better-informed clinical decision making and providing insight into dise
71                        These data may inform clinical decision making and should be the basis for fut
72 liver novel EHR interventions that influence clinical decision making and to support efficient random
73 onary atherosclerosis to guide physicians in clinical decision making and treatment of athletes with
74  patients in the group in whom challenges in clinical decision making are most prevalent.
75 help the triaging of TBI patients and assist clinical decision making at point-of-care settings.
76        Non-clinical factors (NCFs) influence clinical decision making but are rarely considered.
77             This review article seeks to aid clinical decision making by providing an overview of ava
78 od may present a promising avenue to support clinical decision making by providing empirical informat
79 VD and have even increased mortality, making clinical decision making difficult.
80  gained traction as an important adjunct for clinical decision making during vitreoretinal surgery, a
81 ype of approach has the potential to improve clinical decision making for common and rare diseases.Su
82 hors of this commentary provide guidance for clinical decision making for patients with coronavirus d
83          Such insight might be useful in the clinical decision making for those who apply emicizumab
84 al evaluation process, this tool can support clinical decision making for treatment duration.
85 as novel biomarkers for HCM facilitating the clinical decision making in a personalized manner.
86 deep learning aided diagnosis can facilitate clinical decision making in breast cancer by identifying
87 PC3 mutations, there is little data to guide clinical decision making in cases with double mutations.
88  reactivation should prove useful in guiding clinical decision making in HCT recipients.
89  MTRs can and have been validated for use in clinical decision making in malignant diseases, along wi
90 ells.Sig could represent a valuable tool for clinical decision making in patients receiving immunothe
91 mechanisms of action, which could help guide clinical decision making in the management of patients w
92       The role of troponin testing to assist clinical decision making in this setting is unexplored.
93                                              Clinical decision making is extremely difficult in this
94 of key evidence-based medicine principles in clinical decision making is fundamental to preventing ov
95 inimal risk or quality improvement, and when clinical decision making is supported, rather than contr
96 proved in general for both communication and clinical decision making over the 4-week course.
97  literature provide only limited guidance in clinical decision making owing to heterogeneity and scar
98  substitute incorporation is critical in the clinical decision making process and requires special in
99 r of bone graft incorporation and can aid in clinical decision making provided standard radiographic
100 bacterial infections, hopefully facilitating clinical decision making regarding further investigation
101 facilitate perioperative planning and inform clinical decision making regarding post-operative rhythm
102                         This can help inform clinical decision making regarding the need for a right
103 ho diagnose and manage Kawasaki disease, but clinical decision making should be individualized to spe
104                                              Clinical decision making should not be made based on a v
105 simultaneously, to support more personalized clinical decision making than can be made on the basis o
106 osis factor agents and thiopurines to inform clinical decision making when applying TDM in a reactive
107 ving a comprehensive reference to help guide clinical decision making when treating patients.
108 ery by nearly one-third and could help guide clinical decision making with regard to surveillance ver
109 al role of patient values and preferences in clinical decision making, and the development of the met
110 nical trials in T-PLL, and will thus support clinical decision making, as well as the approval of new
111                                           In clinical decision making, in addition to anatomical info
112 the point-of-care, and could help to improve clinical decision making, infection control, and epidemi
113 al and translational applications, including clinical decision making, medical diagnosis, drug repurp
114 roducing processes that facilitate rationale clinical decision making, predictive or prognostic model
115 apeutic approaches play an important role in clinical decision making, treatment guidelines, and heal
116 nt a surgical treatment algorithm to support clinical decision making, with the aim to encourage tran
117 -related information and could be helpful in clinical decision making.
118 ent portion of risk to be routinely used for clinical decision making.
119 h it, and patients are often not involved in clinical decision making.
120 ings need to be confirmed before influencing clinical decision making.
121 n psoriatic arthritis are helpful in guiding clinical decision making.
122 ng of apoptosis post-therapy could assist in clinical decision making.
123 ients when these markers are integrated into clinical decision making.
124 ents having undergone pre-PCI FFR as part of clinical decision making.
125 as used to assess how PSQ results influenced clinical decision making.
126  of clinical decision support to assist with clinical decision making.
127 mes in CS and whether a risk score can guide clinical decision making.
128 ide database for epidemiological studies and clinical decision making.
129 the established biomarkers that are used for clinical decision making.
130 heir use in doctor-patient communication and clinical decision making.
131 owever, there is limited evidence to support clinical decision making.
132 sly reported, and potentially influential in clinical decision making.
133 t incorporate the concept of sarcopenia into clinical decision making.
134 se challenges for diagnosis, treatments, and clinical decision making.
135  radiation and temozolomide and to influence clinical decision making.
136 lesions on root surfaces in order to improve clinical decision making.
137 of the cardiovascular system are central for clinical decision making.
138  are appropriate, and to provide guidance on clinical decision making.
139 would increase the information available for clinical decision making.
140 ieved, potentially providing assistance with clinical decision making.
141          These 2 predictors should influence clinical decision making.
142 ary biomarkers that may be used for specific clinical decision making.
143 nclature for seizure risk stratification and clinical decision making.
144 ded before they can be used as biomarkers in clinical decision making.
145 eatment effects, the most relevant scale for clinical decision making.
146  predicting treatment outcomes and informing clinical decision making.
147  and has the potential to be integrated into clinical decision making.
148 had low-level mutations somehow relevant for clinical decision making.
149 ation, and it can be used as a tool to guide clinical decision making.
150 roaches to capture and account for it during clinical decision making.
151 de a basis for more informed counselling and clinical decision making.
152  prevention clinical trials and personalized clinical decision making.
153 e-clinical models and the potential to guide clinical decision making.
154 ide database for epidemiological studies and clinical decision making.
155 ss or mortality is essential for appropriate clinical decision making.
156 nhance the role of observational research in clinical decision making: (1) improve the quality of ele
157 cts, nurses' autonomy, scope of practice and clinical decision-making abilities.
158 the disease processes will facilitate better clinical decision-making about the therapies offered, ex
159         Therefore, prognostic tools to guide clinical decision-making and avoid overtreatment of indo
160                                      Current clinical decision-making and care pathways must be furth
161                              This can assist clinical decision-making and enable better pre-operative
162 S-CoV-2) serologic assays is needed to guide clinical decision-making and ensure that these assays pr
163 value, 2 - information gained did not impact clinical decision-making and in case of a therapeutic in
164 mation gained was essential and critical for clinical decision-making and in case of a therapeutic in
165 ent, 3 - information gained had an impact on clinical decision-making and in the case of a therapeuti
166 r End-Stage Liver Disease (MELD) is used for clinical decision-making and organ allocation for orthot
167  indicators of organ dysfunction may improve clinical decision-making and outcome of patients.
168 termine the impact of patient preferences on clinical decision-making and outcomes.
169  ascertain prognostic indicators that inform clinical decision-making and practices regarding the rol
170  vivo-derived measurements and could support clinical decision-making and provide surrogate end point
171 ive risk prediction is important for guiding clinical decision-making and resource allocation.
172 f how to best incorporate genomic testing in clinical decision-making and subsequent treatment recomm
173 l-rich tertiary lymphoid structures to guide clinical decision-making and treatments, which could hav
174 me endpoints, absolute changes and impact on clinical decision-making are marginal.
175 nd subsequent graft function is important in clinical decision-making around kidney transplantation,
176 bility of this model may prove beneficial in clinical decision-making both prior to and following tra
177 and treatment of breast cancer have made the clinical decision-making context much more complex.
178                                      Optimal clinical decision-making depends on identification of cl
179  data may be used to quantify risk and guide clinical decision-making for all phenotypes of CS.
180                         Timely and effective clinical decision-making for COVID-19 requires rapid ide
181  been identified, and their integration into clinical decision-making for patients with advanced-stag
182  about the state of fracture repair to guide clinical decision-making for patients.
183  RHR should be integrated into comprehensive clinical decision-making for these patients.
184 e find that good quality AI-based support of clinical decision-making improves diagnostic accuracy ov
185  add incremental diagnostic value but guides clinical decision-making in an unsalutary fashion.
186  scenarios, which can be used as a guide for clinical decision-making in daily practice.
187 ures will increasingly be crucial to guiding clinical decision-making in each patient with cancer.
188                                              Clinical decision-making in kidney transplant (KT) durin
189 vides valuable frameworks and benchmarks for clinical decision-making in patient management, improved
190  in randomized clinical trials and may guide clinical decision-making in patients who experience earl
191 ntial of deep learning to assist and enhance clinical decision-making in patients with AMD, such as e
192 failure is warranted for prognostication and clinical decision-making in the post-cardiac arrest peri
193 n evidence base that is not aligned with how clinical decision-making is actually performed.
194                                     However, clinical decision-making is confounded by the fact that
195 s appraise the patient data set that informs clinical decision-making is unknown.
196      Understanding these risk factors during clinical decision-making may improve prevention of DGF a
197                         SOPCP did not affect clinical decision-making or alter clinical course (grade
198 f peri-implant stability or disease to guide clinical decision-making post-treatment.
199 , advocating for its implementation into the clinical decision-making process besides usual clinical
200 ld be part of routine DMR evaluation and the clinical decision-making process.
201 ntial complementary role in the acute stroke clinical decision-making process.
202 factors, alongside qualitative research into clinical decision-making processes and patients' experie
203                    These findings facilitate clinical decision-making regarding allergic diseases in
204                                              Clinical decision-making regarding the optimization of t
205 on and optimization must remain the basis of clinical decision-making regarding the use of ionizing r
206             Little is known about real-world clinical decision-making related to hospice for members
207 that uses a key features approach to measure clinical decision-making skills and focuses on cases enc
208 r patient engagement, the development of new clinical decision-making support tools, and the validati
209 sk factors can guide appropriate consent and clinical decision-making that may reduce anastomotic-rel
210          To our knowledge, this is the first clinical decision-making tool that generates personalize
211 om randomized controlled trials exist to aid clinical decision-making, and the findings from observat
212 pairments after critical illness could guide clinical decision-making, inform trial enrollment, and f
213  aggressiveness with the potential to impact clinical decision-making, such as targeted biopsy approa
214 riants in these genes should not be used for clinical decision-making, unless accompanied by new and
215 ng can provide information of great value in clinical decision-making, yet RNA from readily available
216 ppropriate Use Criteria were designed to aid clinical decision-making, yet their association with hea
217 re uncommon and therefore unlikely to affect clinical decision-making.
218 ring radiographic workup and integrated into clinical decision-making.
219 hould lead to better risk stratification and clinical decision-making.
220 s to develop tools that can be used to guide clinical decision-making.
221 or aiding in the efficiency and precision of clinical decision-making.
222 -related information and could be helpful in clinical decision-making.
223  tree from the ensemble that can be used for clinical decision-making.
224 ist early and accurate diagnosis and improve clinical decision-making.
225 utility of ML techniques to support informed clinical decision-making.
226 he EHR based on systemic data to assist with clinical decision-making.
227 iculous surgery, endoscopic surveillance and clinical decision-making.
228 o automated medical diagnoses that can guide clinical decision-making.
229 tter inform trial stratification and improve clinical decision-making.
230  SSI versus risk of AKI is needed to improve clinical decision-making.
231 come are not precise enough to guide initial clinical decision-making.
232 iple categories to reflect the complexity of clinical decision-making.
233 l care and to support patient involvement in clinical decision-making.
234 of viremia are urgently needed to accelerate clinical decision-making.
235  MDS/MPN subtypes, which may be relevant for clinical decision-making.
236  of stillbirth risk has potential to support clinical decision-making.
237 uld provide fine-grained resolution to guide clinical decision-making.
238 erred method to generate evidence to support clinical decision-making.
239 , and generalizable performance for enhanced clinical decision-making.
240 cell carcinoma aggressiveness may help guide clinical decision-making.
241 ing personalized treatments and facilitating clinical decision-making.
242 tial prognostic and predictive potential for clinical decision-making.
243 herefore impact FFR measurements and related clinical decision-making.
244 lly significant and therefore able to impact clinical decision-making; and (3) whether DeltaFFR(eng)-
245        Purpose To determine the demographic, clinical, decision-making, and quality-of-life factors t
246 ty of the information provided from them for clinical decision management.
247 et with complex patient histories from which clinical decisions must be made.
248 24 h a day, and it is assumed that important clinical decisions occur continuously around the clock.
249 19 genotype status should not be included in clinical decisions on tamoxifen treatment.
250  preclinical imaging of cancer and informing clinical decisions on therapeutic interventions.
251           The protocols developed focused on clinical decisions regarding intubation, the use of high
252 patient's cancer is evolving in order to aid clinical decisions remains difficult.
253                   We aimed to validate three clinical decision rules (PECARN, CATCH, and CHALICE) in
254                                              Clinical decision rules can help to determine the need f
255                                 Computerized clinical decision support (CCDS) significantly reduced C
256  and unnecessary imaging by mandating use of clinical decision support (CDS) by all providers who ord
257 tive medical decision making, computer-based clinical decision support (CDS) could unlock widespread
258  after, provider overrides of evidence-based clinical decision support (CDS) for ordering computed to
259                                              Clinical decision support (CDS) systems could be used to
260 are platforms have been developed, many with clinical decision support and informatics interoperabili
261 dology and findings could be used to improve clinical decision support and personalize trajectories,
262                Pending legislation specifies clinical decision support before performing advanced ima
263 f FHH with the electronic medical record and clinical decision support capabilities has provided solu
264 ting standardized approaches for introducing clinical decision support has been followed, describing
265 nting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncerta
266                                   Background Clinical decision support is increasingly used to enhanc
267                                     Use of a clinical decision support system (CDSS) might improve ou
268                                         This clinical decision support system identified and utilized
269 udies in which clinicians must interact with clinical decision support system may either exceed or fa
270 sibility and effectiveness of a computerized clinical decision support system to identify pediatric p
271                                   Demand for clinical decision support systems in medicine and self-d
272                  Depending on the particular clinical decision support systems selected within a heal
273           From early-stage drug discovery to clinical decision support systems, we have seen examples
274  that reduce or prevent alert fatigue within clinical decision support systems.
275 ed to reduce or prevent alert fatigue within clinical decision support systems.
276 tests (CT and MRI) that would require use of clinical decision support to achieve Protecting Access t
277 integration on e-prescribing; and (3) use of clinical decision support to assist with clinical decisi
278 dated rapid diet screener tools with coupled clinical decision support to identify actionable modific
279   We implemented and studied the impact of a clinical decision support tool (CDST) to decrease the nu
280 egard artificial intelligence as a promising clinical decision support tool for supine chest radiogra
281 s built in the electronic health record as a clinical decision support tool to enforce protocol compl
282               Such an ultra-early, ECG-based clinical decision support tool, when combined with the j
283 tary conflicts have highlighted the need for clinical decision support tools (CDST) to decrease time
284 e of supporting clinician order entry and of clinical decision support tools (CDSTs) has provided exp
285 atient with OHCA highlight the importance of clinical decision support tools and treatment algorithms
286 ved reporting practices of BCID results with clinical decision support tools providing interpretation
287                                       Use of clinical decision support tools to enable substitution o
288 anding of critical illness, enable real-time clinical decision support, and improve both clinical out
289 els for a variety of applications, including clinical decision support, automated workflow triage, cl
290 e data source for research in intraoperative clinical decision support, risk prediction, or outcomes
291 as electronic medical records augmented with clinical decision support.
292 ue Web site views; 112 users (38%) completed clinical decision support.
293 al benefits of EHR-based research: improving clinical decisions, supporting triage decisions, enablin
294                               Judgements and clinical decisions that occur 'outside' the system inevi
295 epresents the first time that the perplexing clinical decision to choose multiple antibiotics for com
296 cNairy and colleagues highlight the need for clinical decision tools to help identify HIV patients wh
297                       With simple variables, clinical decision trees can be used to distinguish patie
298 stems in healthcare are AI systems that make clinical decisions without human oversight.
299 methods, and can offer improved precision in clinical decision workflows.
300  often used interchangeably to make critical clinical decisions, yet few studies have compared these

 
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