scholarly journals Diabetic macular oedema: evidence-based treatment recommendations for Asian countries

2017 ◽  
Vol 46 (1) ◽  
pp. 75-86 ◽  
Author(s):  
Gemmy CM Cheung ◽  
Young Hee Yoon ◽  
Lee Jen Chen ◽  
Shih Jen Chen ◽  
Tara M George ◽  
...  
Keyword(s):  
Macular Oedema ◽  
Diabetic Macular Oedema ◽  
Treatment Recommendations ◽  
Evidence Based ◽  
Asian Countries ◽  
Evidence Based Treatment

scholarly journals Conformance to Evidence-Based Treatment Recommendations in Schizophrenia Treatment Services

2012 ◽  
Vol 57 (5) ◽  
pp. 317-323 ◽  
Author(s):  
Donald Addington ◽  
Emily McKenzie ◽  
Harvey Smith ◽  
Henry Chuang ◽  
Stephen Boucher ◽  
...  
Keyword(s):  
Treatment Recommendations ◽  
Evidence Based ◽  
Evidence Based Treatment ◽  
Treatment Services

scholarly journals Authors’ response to “Tranexamic acid and uremic bleeding: evidence-based treatment recommendations”

2007 ◽  
Vol 3 (6) ◽  
pp. E3-E3 ◽  
Author(s):  
Stephanie J Hedges ◽  
Sarah B Dehoney ◽  
Justin S Hooper ◽  
Jamshid Amanzadeh ◽  
Anthony J Busti
Keyword(s):  
Tranexamic Acid ◽  
Treatment Recommendations ◽  
Evidence Based ◽  
Evidence Based Treatment

Early experience with IBM Watson for Oncology (WFO) cognitive computing system for lung and colorectal cancer treatment.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 8527-8527 ◽  
Author(s):  
S.P. Somashekhar ◽  
Martín-J. Sepúlveda ◽  
Andrew D Norden ◽  
Amit Rauthan ◽  
Kumar Arun ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Rectal Cancer ◽  
Computing System ◽  
Treatment Recommendations ◽  
Evidence Based ◽  
Metastatic Colon Cancer ◽  
Cognitive Computing ◽  
Evidence Based Treatment ◽  
Tumour Board ◽  
Ibm Watson

8527 Background: IBM Watson for Oncology is an artificial intelligence cognitive computing system that provides confidence-ranked, evidence-based treatment recommendations for cancer. In the present study, we examine the level of agreement for lung and colorectal cancer therapy between the multidisciplinary tumour board from Manipal Comprehensive Cancer Centre in Bangalore, India, and Watson for Oncology. Methods: Watson for Oncology is a Memorial Sloan Kettering Cancer Center (New York, USA) trained cognitive computing system that uses natural language processing and machine learning to provide treatment recommendations. It processes structured and unstructured data from medical literature, treatment guidelines, medical records, imaging, lab and pathology reports, and the expertise of Memorial Sloan Kettering experts to formulate therapeutic recommendations. Treatment recommendations are provided in three categories: recommended, for consideration and not recommended. In this report we provide the results of the independent and blinded evaluation by the multidisciplinary tumour board and Watson for Oncology of 362 total cancer cases comprised of 112 lung, 126 colon and 124 rectal cancers seen at the Centre within the last three years. The recommendations of the two agents were compared for agreement and considered concordant when the tumour board recommendation was included in the recommended or for consideration categories of the treatment advisor. Results: Overall, treatment recommendations were concordant in 96.4% of lung, 81.0% of colon and 92.7% of rectal cancer cases. By tumour stage, treatment recommendations were concordant in 88.9% of localized and 97.9% of metastatic lung cancer, 85.5% of localized and 76.6% of metastatic colon cancer, and 96.8% of localized and 80.6% of metastatic rectal cancer. Conclusions: Treatment recommendations made by the Manipal multidisciplinary tumour board and Watson for Oncology were highly concordant in the cancers examined. This cognitive computing technology holds much promise in helping oncologists make information intensive, evidence based treatment decisions.

Comments on Yaruss, LaSalle, and Conture (1998)

2000 ◽  
Vol 9 (2) ◽  
pp. 162-165 ◽  
Author(s):  
Anne K. Cordes
Keyword(s):  
Relevant Information ◽  
Treatment Recommendations ◽  
Evidence Based ◽  
Evidence Based Treatment ◽  
Training Clinic ◽  
Minimal Information ◽  
Assessment And Diagnosis

In summary, Yaruss et al. have contributed to the literature a set of “recommendation categories” that were based on retrospective analyses of minimal information from children who might have stuttered, gathered originally by student clinicians in one training clinic who were entirely unaware that their data would be used to develop recommendations for other children in other clinics. These categories and recommendations might have led to inaccurate decisions about a significant minority of the children involved in this study (Yaruss et al.’s Footnote 1), but “suggested treatment recommendations” are nevertheless presented as “useful for clinicians to consider” (p. 73).Given some indications that delaying treatment for children who stutter may reduce the effectiveness of any eventual treatment (R. J. Ingham & Cordes, 1999), the decision not to recommend treatment after an initial stuttering evaluation is an important one. The evidence used to support such a decision should be strong, developed from well-designed, carefully executed, and conservatively interpreted studies. Yaruss et al. (1998) presented some information for clinicians to consider, and other relevant information has been presented in other recent forums (e.g., Curlee & Yairi, 1997, and commentaries). I suggest only that clinicians might also want to consider that the literature on stuttering assessment and diagnosis is already full of data-based information and some relatively carefully developed recommendations (see Adams, 1980; Costello & R. J. Ingham, 1984; Culatta & Goldberg, 1995; Curlee, 1993; J. C. Ingham & Riley, 1998; Ryan, 1992). It is critical to note, as well, that essentially all of these sources would recommend treatment for more children sooner than the recommendations presented by Yaruss et al. would do. The phrase “evidence-based treatment” has become popular in the time since Yaruss et al.’s (1998) article was published; the point for children who stutter, and for all of our other clients, is that all of our decisions and recommendations should always

Evidence-based treatment recommendations for uremic bleeding

2007 ◽  
Vol 3 (3) ◽  
pp. 138-153 ◽  
Author(s):  
Stephanie J Hedges ◽  
Sarah B Dehoney ◽  
Justin S Hooper ◽  
Jamshid Amanzadeh ◽  
Anthony J Busti
Keyword(s):  
Treatment Recommendations ◽  
Evidence Based ◽  
Evidence Based Treatment

scholarly journals What are evidence-based treatment recommendations?

2011 ◽  
Vol 20 (1) ◽  
pp. 29-31 ◽  
Author(s):  
C. Barbui ◽  
A. Cipriani
Keyword(s):  
Clinical Practice ◽  
Treatment Recommendations ◽  
Evidence Based ◽  
Primary Research ◽  
Everyday Clinical Practice ◽  
Evidence Based Treatment ◽  
Assessment Development ◽  
New Methodologies ◽  
Grade Methodology

In recent years new methodologies for developing treatment recommendations that give consideration to evidence, values, preferences and feasibility issues have been developed. One of the most well-developed approaches is theGrading of Recommendations Assessment, Development and Evaluation(GRADE) methodology. This article briefly presents how this methodology may be employed to develop treatment recommendations that might constitute a permanent infrastructure between primary research and everyday clinical practice.

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