Contents Introduction Procedure The Results Conclusion

Contents Introduction Procedure Conclusion

Background The rapid growth of the e.Publications.The rapid growth of the e.Publications. Most web search engines provide knowledge from different areas.Most web search engines provide knowledge from different areas. –e.g. Google.com Some databases are focusing on specialized fields. Some databases are focusing on specialized fields. –e.g. PubMed Each search engine is ranking the results depending on pre-defined factors.Each search engine is ranking the results depending on pre-defined factors. 4

The Problem The variety of ranking and classifications might hinder the researchers’ work.The variety of ranking and classifications might hinder the researchers’ work. Researchers and doctors spend long time to read all articles to find strong evidence to support their work.Researchers and doctors spend long time to read all articles to find strong evidence to support their work. Beginner researchers’ decisions might be affected by relying on articles have low – quality with high – ranking or low-quality with large number of citations.Beginner researchers’ decisions might be affected by relying on articles have low – quality with high – ranking or low-quality with large number of citations. The need to study and evaluate the popular ranking systems to focus on the most important publications.The need to study and evaluate the popular ranking systems to focus on the most important publications. 5

Project Aim Test the rank of 2,075 medical publications in four web ranking systems and a specialized citation index, which are:Test the rank of 2,075 medical publications in four web ranking systems and a specialized citation index, which are: –Google PageRank. –Yahoo WebRank. –Bing ranking systems. –Alexa ranking systems. –PubMed citation index. Compare the results with the Strength of Recommendation Taxonomy ( SORT ) methodology.Compare the results with the Strength of Recommendation Taxonomy ( SORT ) methodology. 6

The Strength of Recommendation Taxonomy (SORT) The SORT classification has been designed to determine the strength of evidences in the medical publications.The SORT classification has been designed to determine the strength of evidences in the medical publications. SORT uses three criteria to classify the publications:SORT uses three criteria to classify the publications: –The strength of recommendation for the body of evidence. A  consistent and high quality patient-oriented evidence.A  consistent and high quality patient-oriented evidence. B  inconsistent or limited quality patient-oriented evidence.B  inconsistent or limited quality patient-oriented evidence. C  opinions, disease-oriented, consensus and usual practice as well as case studies for treatment, diagnosis, screening or prevention.C  opinions, disease-oriented, consensus and usual practice as well as case studies for treatment, diagnosis, screening or prevention. –The quality of the individual studies. ( 1, 2, 3 ). –The consistency of the publication. ( Consistent or inconsistent ) 7

Contents Introduction Procedure The Results Conclusion

Development Steps 9

Step 1: Generating the PubMed Publications’ Links PubMed Publications’ ID have been given.PubMed Publications’ ID have been given. Automatic links generating.Automatic links generating. The link =The link = + Publication ID 10 The Given Data The Generated links

Steps 2-5: Testing Web Ranking Systems Automatic links testing framework have been developed.Automatic links testing framework have been developed. Pre-developed script has been used.Pre-developed script has been used. AJAX technologies have been used to improve the test speed.AJAX technologies have been used to improve the test speed. 11

Step 6: Testing PubMed Database PubMed citation index does not provide web service to implement the test.PubMed citation index does not provide web service to implement the test. The PubMed publication pages have been studied to develop an automated method to fetch the number of citations.The PubMed publication pages have been studied to develop an automated method to fetch the number of citations. 12

Step 6: Testing PubMed Database 13 Example of PubMed Page

Step 6: Testing PubMed Database 14 Example of PubMed Citation Format The pages have been copied and analysed automatically.The pages have been copied and analysed automatically. The system is able to process around 20 links per test.The system is able to process around 20 links per test. Some articles do not have citations.Some articles do not have citations.

Step 6: Testing PubMed Database 15 Example of PubMed test results

Contents Introduction Procedure The Results Conclusion

Google PageRank Results 17 Google PageRank Results Publication rank Number of Publications

Google PageRank Results The correlation between the SORT classification and Google PageRank has been calculated using Pearson, Spearman and Kendall Correlation Coefficients as the following: –Pearson Correlation = –Spearman Rank Correlation = –Kendall Correlation =

Yahoo, Bing and Alexa Results Yahoo assigned all results to zero.Yahoo assigned all results to zero. Bing assigned all results to zero.Bing assigned all results to zero. Alexa assigned all results as same as the root page; which is:Alexa assigned all results as same as the root page; which is: e.g. In 2 / November / 2011, the rank of e.g. In 2 / November / 2011, the rank of was  all publications’ ranks =

PubMed Results 20 Google PageRank Results Number of Citations Number of Publications

PubMed Results The correlation between the SORT classification and PubMed citation index has been calculated among Pearson, Spearman and Kendall Correlation Coefficients as the following: –Pearson Correlation = –Spearman Rank Correlation = –Kendall Correlation =

Results comparison 22 CorrelationGoogle PageRankPubMed Pearson Spearman Kendall

Contents Introduction Procedure The Results Conclusion

Conclusion This study attempted to test whether the web ranking systems and citation indexes can help to determine the strength of 2,075 clinical publications according to their SORT classifications. Three generic measures have been used to calculate the correlations. Neither publication’s rank in the four popular systems nor the number of citations in PubMed database are correlated to the Strength Of Recommendation Taxonomy. 24

Recommendations Studying other citation indexes such as Google Scholar, ScienceDirect, and Scopus. Track the positions of the authors who rank the publications and putting the publication date into the consideration 25

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